Blood transcriptional signatures of active pulmonary tuberculosis and sarcoidosis

ABSTRACT

The present invention includes a method of determining a lung disease from a patient suspected of sarcoidosis, tuberculosis, lung cancer or pneumonia comprising: obtaining a sample from whole blood of the patient suspected of sarcoidosis, tuberculosis, lung cancer or pneumonia; detecting expression of (although not exclusive) six or more disease genes, markers, or probes selected from SEQ ID NOS.: 1 to 1446, wherein increased expression of mRNA of upregulated sarcoidosis, tuberculosis, lung cancer and pneumonia markers of SEQ ID NOS.: 1 to 1446 and/or decreased expression of mRNA of downregulated sarcoidosis, tuberculosis, lung cancer or pneumonia markers of SEQ ID NOS.: 1 to 1446 relative to the expression of the mRNAs from a normal sample; and determining the lung disease based on the expression level of the six or more disease markers of SEQ ID NOS.: 1 to 1446 based on a comparison of the expression level of sarcoidosis, tuberculosis, lung cancer, and pneumonia.

CROSS-REFERENCE TO RELATED APPLICATIONS

None.

TECHNICAL FIELD OF THE INVENTION

The present invention relates in general to the field of medical diagnosis and medical treatment, and more particularly, to a novel blood transcriptional signatures to distinguish between active pulmonary tuberculosis, sarcoidosis, lung cancer and pneumonia.

STATEMENT OF FEDERALLY FUNDED RESEARCH

None.

INCORPORATION-BY-REFERENCE OF MATERIALS

A number of lengthy tables are included herewith and the content incorporated herein by reference. The text file Symbol-Regulation-ID.txt is 47 Kb, Symbol-Sequence-ID.txt is 92 Kb, and 1359-List.txt is 88 Kb and are filed herewith and incorporated by reference in their entirety.

LENGTHY TABLES The patent application contains a lengthy table section. A copy of the table is available in electronic form from the USPTO web site (http://seqdata.uspto.gov/?pageRequest=docDetail&DocID=US20150315643A1). An electronic copy of the table will also be available from the USPTO upon request and payment of the fee set forth in 37 CFR 1.19(b)(3).

BACKGROUND OF THE INVENTION

Without limiting the scope of the invention, its background is described in connection with transcriptional signatures. Over nine million new cases of active tuberculosis (TB), and 1.4 million deaths from TB, are estimated to occur around the world every year (1). One of the difficulties of curing pulmonary TB is the ability to diagnose the disease from other similar pulmonary diseases such as pulmonary sarcoidosis, community acquired pneumonia and lung cancer. TB and sarcoidosis are widespread multisystem diseases that preferentially involve the lung and present in a very similar clinical, radiological and histological manner. Distinguishing these diseases therefore often requires an invasive biopsy.

Granuloma formation is fundamental to both these diseases and although the aetiology of TB is well-recognised as the pathogen Mycobacterium tuberculosis, the predominant cause of sarcoidosis remains unknown (2). The underlying pathways of granulomatous inflammation are also poorly understood and there is little understanding of disease-specific differences. Both sarcoidosis and TB can affect adults within the same age group, who then present with similar pulmonary symptoms and radiological thoracic abnormalities (3, 4). TB can also display a similar presentation to other pulmonary infectious diseases such as community acquired pneumonia and other lung inflammatory disorders such as primary lung cancer. Due to the complexity of these diseases a systems biology approach offers the ability to help unravel the principal host immune responses. Peripheral blood has the capacity to reflect pathological and immunological changes in the body, and identification of disease-associated alterations can be determined by a blood transcriptional signature (5). In addition the applicants have published a IFN-inducible neutrophil blood transcriptional signature in active TB patients that is absent in the majority of latent individuals and healthy controls, that correlates significantly with the extent of lung radiographic disease (5) and is diminished upon treatment (5, 12).

Blood gene expression profiling has been successfully applied to other infectious and inflammatory disorders, such as systemic lupus erythematosus (SLE), to help understand disease mechanisms and improve diagnosis and treatment (5). Two recent studies have used blood transcriptional profiling for the comparison of pulmonary TB and sarcoidosis; both studies found the diseases had similar transcriptional responses, which involved the overexpression of IFN-inducible genes (9, 10). However, these studies did not differentiate signatures from other pulmonary diseases leaving to question if the transcriptional signatures were non-specific for pulmonary disorders.

SUMMARY OF THE INVENTION

In one embodiment, the present invention includes a method of determining if a human subject is afflicted with pulmonary disease comprising: obtaining a sample from a subject suspected of having a pulmonary disease; determining the expression level of six or more genes from each of the following genes expressed in one or more of the following expression pathways: EIF2 signaling; mTOR signaling; regulation of eIF4 and p70s6K signaling; interferon signaling; antigen presentation pathways; T cell signaling pathways; and other signaling pathways; comparing the expression level of the six or more genes with the expression level of the same genes from individuals not afflicted with a pulmonary disease, and determining the level of expression of the six or more genes in the sample from the subject relative to the samples from individuals not afflicted with a pulmonary disease for the genes expressed in the one or more expression pathways, wherein co-expression of genes in the EIF2 signaling and mTOR signaling pathways are indicative of active sarcoidosis; co-expression of genes in the regulation of eIF4 and p70s6K signaling pathways is indicative of pneumonia; co-expression of genes in the interferon signaling and antigen presentation pathways are indicative of tuberculosis; and co-expression of genes in the T cell signaling pathways; and other signaling pathways is indicative of lung cancer. In one aspect, the genes associated with tuberculosis are selected from at least 3, 4, 5 or 6 genes selected from ANKRD22; FCGR1A; SERPING1; BATF2; FCGR1C; FCGR1B; LOC728744; IFITM3; EPSTI1; GBP5; IF144L; GBP6; GBP1; LOC400759; IFIT3; AIM2; SEPT4; C1QB; GBP1; RSAD2; RTP4; CARD17; IFIT3; CASP5; CEACAM1; CARD17; ISG15; IF127; TIMM10; WARS; IF16; TNFAIP6; PSTPIP2; IF144; SCO2; FBXO6; FER1L3; CXCL10; DHRS9; OAS1; STAT1; HP; DHRS9; CEACAM1; SLC26A8; CACNA1E; OLFM4; and APOL6, wherein the genes are evaluated at least one of: in aggregate, in the order listed, aggregated into pathways, or selected from 7, 8, 9, 10, 11, 12, 13, 15, 20, 25, 35, 40, 45, or 49 genes. In another aspect, the genes associated with tuberculosis and not active sarcoidosis, pneumonia or lung cancer are selected from C1QB; IF127; SMARCD3; SOCS1; KCNJ15; LPCAT2; ZDHHC19; FYB; SP140; IFITM1; ALAS2; CEACAM6; OAS2; C1QC; LOC100133565; ITGA2B; LY6E; SP140; CASP7; GADD45G; FRMD3; CMPK2; AQP10; CXCL14; ITPRIPL2; FAS; XK; CARD16; SLAMF8; SELP; NDN; OAS2; TAPBP; BPI; DHX58; GAS6; CPT1B; CD300C; LILRA6; USF1; C2; 38231.0; NFXL1; GCH1; CCR1; OAS2; CCR2; F2RL1; SNX20; and ARAP2, wherein the genes are evaluated at least one of: in aggregate, in the order listed, aggregated into pathways, or selected from 7, 8, 9, 10, 11, 12, 13, 15, 20, 25, 35, 40, 45, or 49 genes. In another aspect, the genes associated with active sarcoidosis are selected from FCGR1A; ANKRD22; FCGR1C; FCGR1B; SERPING1; FCGR1B; BATF2; GBP5; GBP1; IFIT3; ANKRD22; LOC728744; GBP1; EPSTI1; IF144L; INDO; IFITM3; GBP6; RSAD2; DHRS9; TNFAIP6; IFIT3; P2RY14; DHRS9; IDO1; STAT1; WARS; TIMM10; P2RY14; LOC389386; FER1L3; IFIT3; RTP4; SCO2; GBP4; IFIT1; LAP3; OASL; CEACAM1; LIMK2; CASP5; STAT1; CCL23; WARS; ATF3; IF16; PSTPIP2; ASPRV1; FBXO6; and CXCL10, wherein the genes are evaluated at least one of: in aggregate, in the order listed, aggregated into pathways, or selected from 7, 8, 9, 10, 11, 12, 13, 15, 20, 25, 35, 40, 45, or 49 genes. In another aspect, the genes associated with active sarcoidosis and not tuberculosis, pneumonia or lung cancer are selected from CCL23; PIK3R6; EMR4; CCDC146; KLF4; GRINA; SLC4A1; PLA2G7; GRAMD1B; RAPGEF1; NXNL1; TRIM58; GABBR1; TAGLN; KLF4; MFAP3L; LOC641798; RIPK2; LOC650840; FLJ43093; ASAP2; C15orf26; REC8; KIAA0319L; GRINA; FLJ30092; BTN2A1; HIF1A; LOC440313; HOXA1; LOC645153; ST3GAL6; LONRF1; PPP1R3B; MPPE1; LOC652699; LOC646144; SGMS1; BMP2K; SLC31A1; ARSB; CAMK1D; ICAM4; HIF1A; LOC641996; RNASE10; PI15; SLC30A1; LOC389124; and ATP1A3, wherein the genes are evaluated at least one of: in aggregate, in the order listed, aggregated into pathways, or selected from 7, 8, 9, 10, 11, 12, 13, 15, 20, 25, 35, 40, 45, or 49 genes. In another aspect, the genes associated with pneumonia are selected from OLFM4; LTF; VNN1; HP; DEFA4; OPLAH; CEACAM8; DEFA1B; ELANE; C19orf59; ARG1; CDK5RAP2; DEFA1B; DEFA3; DEFA1B; FCGR1A; MMP8; FCGR1B; SLPI; SLC26A8; MAPK14; CAMP; NLRC4; FCAR; RNASE3; FCGR1B; NAIP; OLR1; FCGR1C; ANXA3; DEFA1; PGLYRP1; TCN1; ANKDD1A; COL17A1; SLC26A8; TMEM144; SAMD14; MAPK14; RETN; NAIP; GPR84; CASP5; MPO; MMP9; CR1; MYL9; CLEC4D; ITGAX; and ANKRD22, wherein the genes are evaluated at least one of: in aggregate, in the order listed, aggregated into pathways, or selected from 7, 8, 9, 10, 11, 12, 13, 15, 20, 25, 35, 40, 45, or 49 genes. In another aspect, the genes associated with pneumonia and not tuberculosis, active sarcoidosis, or lung cancer are selected from DEFA4; ELANE; MMP8; OLR1; COL17A1; RETN; GPR84; LOC100134379; TACSTD2; SLC2A11; LOC100130904; MCTP2; AZU1; DACH1; GADD45A; NSUN7; CR1; CDK5RAP2; LOC284648; GPR177; CLEC5A; UPB1; SLC2A5; GPR177; APP; LAMC1; REPS2; PIK3CB; SMPDL3A; UBE2C; NDUFAF3; CDC20; CTSK; RAB13; LOC651524; TMEM176A; PDGFC; ATP9A; SV2A; SPOCD1; MARCO; CCDC109A; NUSAP1; SLCO4C1; CYP27A1; LOC644615; PKM2; BMX; PADI4; and NAMPT, wherein the genes are evaluated at least one of: in aggregate, in the order listed, aggregated into pathways, or selected from 7, 8, 9, 10, 11, 12, 13, 15, 20, 25, 35, 40, 45, or 49 genes. In another aspect, the genes associated with lung cancer are selected from ARG1; TPST1; FCGR1A; C19orf59; SLPI; FCGR1B; IL1R1; FCGR1C; TDRD9; SLC26A8; FCGR1B; CLEC4D; LOC100132858; SLC22A4; LOC100133177; SIPA1L2; ANXA3; LIMK2; TMEM88; MMP9; ASPRV1; MANSC1; TLR5; CD163; CAMP; LOC642816; DPRXP4; LOC643313; NTN3; MRVI1; F5; SOCS3; TncRNA; MIR21; LOC100170939; LOC100129904; GRB10; ASGR2; LOC642780; LOC400499; FCAR; KREMEN1; SLC22A4; CR1; LOC730234; SLC26A8; C7orf53; VNN1; NLRC4; and LOC400499, wherein the genes are evaluated at least one of: in aggregate, in the order listed, aggregated into pathways, or selected from 7, 8, 9, 10, 11, 12, 13, 15, 20, 25, 35, 40, 45, or 49 genes. In another aspect, the genes associated with lung cancer and not tuberculosis, active sarcoidosis, or pneumonia are selected from TPST1; MRVI1; C7orf53; ECHDC3; LOC651612; LOC100134660; TIAM2; KIAA1026; HECW2; TLE3; TBC1D24; LOC441193; CD163; RFX2; LOC100134688; LOC642342; FKBP9L; PHF20L1; LOC402176; CD163; OSBPL1A; PRMT5; UBTD1; ADORA3; SH2D3C; RBP7; ERGIC1; TMEM45B; CUX1; TREM1; C1GALT1C1; MAML3; C15orf29; DSC2; RRP12; LRP3; HDAC7A; FOS; C14orf4; LIPN; MAP1LC3B2; LOC400793; LOC647834; PHF20L1; CCNJL; SLC12A6; FLJ42957; CCDC147; SLC25A40; and LOC649270, wherein the genes are evaluated at least one of: in aggregate, in the order listed, aggregated into pathways, or selected from 7, 8, 9, 10, 11, 12, 13, 15, 20, 25, 35, 40, 45, or 49 genes. In another aspect, the genes associated with lung cancer and not tuberculosis, active sarcoidosis, or pneumonia are selected from wherein the genes associated with lung cancer and not tuberculosis, active sarcoidosis, or pneumonia are selected from Table 1 by: parsing the genes into the expression pathways, and determining that the subject is afflicted with a pulmonary disease selected from tuberculosis, sarcoidosis, cancer or pneumonia based on the gene expression from a sample obtained from the subject when compared to the level of expression of the genes in each of the expression pathways. In another aspect, the specificity is 90 percent or greater and sensitivity is 80 percent or greater for a diagnosis of tuberculosis or sarcoidosis. In another aspect, the method further comprises a method for displaying if the patient has tuberculosis or sarcoidosis aggregating the expression data from the 3, 4, 5, 6 or more genes into a single visual display of a vector of expression for tuberculosis, sarcoidosis, cancer or an infectious pulmonary disease. In another aspect, the method further comprises the step of detecting and evaluating 7, 8, 9, 10, 12, 15, 20, 25, 35, 50, 75, 90, 100, 125, or 144 genes for the analysis. In another aspect, the method further comprises the step of detecting and evaluating the EIF2 signaling; mTOR signaling; regulation of eIF4 and p70s6K signaling; interferon signaling; antigen presentation pathways; T cell signaling pathways; and other signaling pathways from 7, 8, 9, 10, 12, 15, 20, 25, 35, 50, 75, 90, 100, 125, or 144 genes that are upregulated or downregulated and are selected from UBE2J2; ALPL; JMJD6; FCER1G; LILRA5; LY96; FCGR1C; C10orf33; GPR109B; PROK2; PIM3; SH3GLB1; DUSP3; PPAP2C; SLPI; MCTP1; KIF1B; FLJ32255; BAGE5; IFITM1; GPR109A; IF135; LOC653591; KREMEN1; IL18R1; CACNA1E; ABCA2; CEACAM1; MXD4; TncRNA; LMNB1; H2AFJ; HP; ZNF438; FCER1A; SLC22A4; DISC1; MEFV; ABCA1; ITPRIPL2; KCNJ15; LOC728519; ERLIN1; NLRC4; B4GALT5; LOC653610; HIST2H2BE; AIM2; P2RY10; CCR3; EMR4P; NTN3; C1QB; TAOK1; FCGR1B; GATA2; FKBP5; DGAT2; TLR5; CARD17; INCA; MSL3L1; ESPN; LOC645159; C19orf59; CDK5RAP2; PLSCR1; RGL4; IFI30; LOC641710; GAGGCTTTCAGGTAGGAGGACAATGGTAGCACTGTAGGTCCCCAGTGTCG (SEQ ID NO.: 754); LOC100008589; LOC100008589; SMARCD3; NGFRAP1; LOC100132394; OPLAH; CACNG6; LILRB4; HIST2H2AA4; CYP1B1; PGS1; SPATA13; PFKFB3; HIST1H3D; SNORA73B; SLC26A8; SULT1B1; ADM; HIST2H2AA3; HIST2H2AA3; GYG1; CST7; EMR4; LILRA6; MEF2D; IFITM3; MSL3; DHRS13; EMR4; C16orf57; HIST2H2AC; EEF1D; TDRD9; GPR97; ZNF792; LOC100134364; SRGAP3; FCGR1A; HPSE; LOC728417; LOC728417; MIR21; HIST1H2BG; COP1; SMARCD3; LOC441763; ZSCAN18; GNG8; MTRF1L; ANKRD33; PLAC8; PLAC8; SLC26A8; AGTRAP; FLJ43093; LPCAT2; AGTRAP; S100A12; SVIL; LILRA5; LILRA5; ZFP91; CLC; LOC100133565; LTB4R; SEPT04; ANXA3; BHLHB2; IL4R; IFNAR1; MAZ; GCCCCCTAATTGACTGAATGGAACCCCTCTTGACCAAAGTGACCCCAGAA (SEQ ID NO.: 1379); OSM; and optionally excluding at least one of ADM, SEPT4, IFITM1, FCER1G, MED2F, CDK5RAP2 or CARD16. In another aspect, the genes that are downregulated are selected from MEF2D; BHLHB2; CLC; FCER1A; SRGAP3; FLJ43093; CCR3; EMR4; ZNF792; C10orf33; CACNG6; P2RY10; GATA2; EMR4P; ESPN; EMR4; MXD4; and ZSCAN18. In another aspect, the interferon inducible genes are selected from CD274; CXCL10; GBP1; GBP2; GBP5; IF116; IF135; IF144; IF144L; IF16; IFIH1; IFIT2; IFIT3; IFIT5; IFITM1; IFITM3; IRF7; OAS1; OAS2; OAS3; SOCS1; STAT1; STAT2; TAP1; and TAP2. In another aspect, the sample is a blood, peripheral blood mononuclear cells, sputum, or lung biopsy. In another aspect, the expression level comprises a mRNA expression level and is quantitated by a method selected from the group consisting of polymerase chain reaction, real time polymerase chain reaction, reverse transcriptase polymerase chain reaction, hybridization, probe hybridization and gene expression array. In another aspect, the expression level is determined using at least one technique selected from the group consisting of polymerase chain reaction, heteroduplex analysis, single stand conformational polymorphism analysis, ligase chain reaction, comparative genome hybridization, Southern blotting, Northern blotting, Western blotting, enzyme-linked immunosorbent assay, fluorescent resonance energy-transfer and sequencing. In another aspect, the expression level is determined by microarray analysis that comprises use of oligonucleotides that hybridize to mRNA transcripts or cDNAs for the six or more genes, and wherein the oligonucleotides are disposed or directly synthesized on the surface of a chip or wafer. In another aspect, the oligonucleotides are about 10 to about 50 nucleotides in length. In another aspect, the method further comprises the step of using the determined comparative gene product information to formulate at least one of diagnosis, a prognosis or a treatment plan. In another aspect, the patient's disease state is further determined by radiological analysis of the patient's lungs. In another aspect, the method further comprises the step of determining a treated patient gene expression dataset after the patient has been treated and determining if the treated patient gene expression dataset has returned to a normal gene expression dataset thereby determining if the patient has been treated.

Another embodiment of the present invention includes a method of determining a lung disease from a patient suspected of sarcoidosis, tuberculosis, lung cancer or pneumonia comprising: obtaining a sample from the patient suspected of sarcoidosis, tuberculosis, lung cancer or pneumonia; detecting expression of 3, 4, 5, 6 or more disease genes, markers, or probes of Table 1 (SEQ ID NOS.: 1 to 1446), wherein increased expression of mRNA of upregulated sarcoidosis, tuberculosis, lung cancer and pneumonia markers of Table 1 and/or decreased expression of mRNA of downregulated sarcoidosis, tuberculosis, lung cancer or pneumonia markers of Table 1 relative to the expression of the mRNAs from a normal sample; and determining the lung disease based on the expression level of the six or more disease markers of Table 1 based on a comparison of the expression level of sarcoidosis, tuberculosis, lung cancer, and pneumonia. In one aspect, the method further comprises the step of selecting 3, 4, 5, 6 or more genes that are differentially expressed between sarcoidosis, tuberculosis, lung cancer, and pneumonia. In another aspect, the method further comprises the step of differentiating between sarcoidosis that is active sarcoidosis and inactive sarcoidosis by determining the expression levels of six or more genes, markers, or probes selected from: TMEM144; FBLN5; FBLN5; ERI1; CXCR3; GLUL; LOC728728; KLHDC8B; KCNJ15; RNF125; CCNB1IP1; PSG9; LOC100170939; QPCT; CD177; LOC400499; LOC400499; LOC100134634; TMEM88; LOC729028; EPSTI1; INSC; LOC728484; ERP27; CCDC109A; LOC729580; C2; TTRAP; ALPL; MAEA; COX10; GPR84; TRMT11; ANKRD22; MATK; TBC1D24; LILRA5; TMEM176B; CAMP; PKIA; PFTK1; TPM2; TPM2; PRKCQ; PSTPIP2; LOC129607; APRT; VAMPS; FCGR1C; SHKBP1; CD79B; SIGIRR; FKBP9L; LOC729660; WDR74; LOC646434; LOC647834; RECK; MGST1; PIWIL4; LILRB1; FCGR1B; NOC3L; ZNF83; FCGBP; SNORD13; LOC642267; GBP5; EOMES; BST1; C5; CHMP7; ETV7; ILVBL; LOC728262; GNLY; LOC388572; GATA1; MYBL1; LOC441124; LOC441124; IL12RB1; BRIX1; GAS6; GAS6; LOC100133740; GPSM1; C6orf129; IER3; MAPK14; PROK1; GPR109B; SASP; LOC728093; PROK2; CTSW; ABHD2; LOC100130775; SLITRK4; FBXW2; RTTN; TAF15; FUT7; DUSP3; LOC399715; LOC642161; LOC100129541; TCTN1; SLAMF8; TGM2; ECE1; CD38; INPP4B; ID3; CR1; CR1; TAPBP; PPAP2C; MBOAT2; MS4A2; FAM176B; LOC390183; SERPING1; LOC441743; H1F0; SOD2; LOC642828; POLB; TSPAN9; ORMDL3; FER1L3; LBH; PNKD; SLPI; SIRPB1; LOC389386; REC8; GNLY; GNLY; FOLR3; LOC730286; SKAP1; SELP; DHX30; KIAA1618; NQO2; ANKRD46; LOC646301; LOC400464; LOC100134703; C20orf106; SLC25A38; YPEL1; IL1R1; EPHA1; CHD6; LIMK2; LOC643733; LOC441550; MGC3020; ANKRD9; NOD2; MCTP1; BANK1; ZNF30; FBXO7; FBXO7; ABLIM1; LAMP3; CEBPE; LOC646909; BCL11B; TRIM58; SAMD3; SAMD3; MYOF; TTPAL; LOC642934; FLJ32255; LOC642073; CAMKK2; OAS2; RASGRP1; CAPG; LOC648343; CETP; CETP; CXCR7; UBASH3A; LOC284648; IL1R2; AGK; GTPBP8; LEF1; LEF1; GPR109A; IF135; IRF7; IRF7; SP4; IL2RB; ABLIM1; TAPBP; MAL; TCEA3; KREMEN1; KREMEN1; VNN1; GBP1; GBP1; UBE2C; DET1; ANKRD36; DEFA4; GCH1; IL7R; TMCO3; FBXO6; LACTB; LOC730953; LOC285296; IL18R1; PRR5; LOC400061; TSEN2; MGC15763; SH3YL1; ZNF337; AFF3; TYMS; ZCCHC14; SLC6A12; LY6E; KLF12; LOC100132317; TYW3; BTLA; SLC24A4; NCALD; ORAI2; ITGB3BP; GYPE; DOCKS; RASGRP4; LOC339290; PRF1; TGFBR3; LGALS9; LGALS9; BATF2; MGC57346; TXK; DHX58; EPB41L3; LOC100132499; LOC100129674; GDPD5; ACP2; C3AR1; APOB48R; UTRN; SLC2A14; CLEC4D; PKM2; CDCA5; CACNA1E; OSBPL3; SLC22A15; VPREB3; LOC642780; MEGF6; LOC93622; PFAS; LOC729389; CREBZF; IMPDH1; DHRS3; AXIN2; DDX60L; TMTC1; ABCA2; CEACAM1; CEACAM1; FLJ42957; SIAH2; DDAH2; C13orf18; TAGLN; LCN2; RELB; NR1I2; BEND7; PIK3C2B; IF16; DUT; SETD6; LOC100131572; TNRC6A; LOC399744; MAPK13; TAP2; CCDC15; TncRNA; SIPA1L2; HIST1H4E; PTPRE; ELANE; TGM2; ARSD; LOC651451; CYFIP1; CYFIP1; LOC642255; ASCC2; ZNF827; STAB1; LMNB1; MAP4K1; PSMB9; ATF3; CPEB4; ATP5S; CD5; SYTL2; H2AFJ; HP; SORT1; KLHL18; HIST1H2BK; KRTAP19-6; RNASE2; LOC100134393; C11orf82; BLK; CD160; LOC100128460; CD19; ZNF438; MBNL3; MBNL3; LOC729010; NAGA; FCER1A; C6orf25; SLC22A4; LOC729686; CTSL1; BCL11A; ACTA2; KIAA1632; UBE2C; CASP4; SLC22A4; SFT2D2; TLR2; C10orf105; EIF2AK2; TATDN1; RAB24; FAH; DISC1; LOC641848; ARG1; LCK; WDFY3; RNF165; MLKL; LOC100132673; ANKDD1A; MSRB3; LOC100134379; MEFV; C12orf57; CCDC102A; LOC731777; LOC729040; TBC1D8; KLRF1; KLRF1; ABCA1; LOC650761; LOC653867; LOC648710; SLC2A11; LOC652578; GPR114; MANSC1; MANSC1; DGKA; LIN7A; ITPRIPL2; ANO9; KCNJ15; KCNJ15; LOC389386; LOC100132960; LOC643332; SF11; ABCE1; ABCE1; SERPINA1; OR2W3; ABI3; LOC400759; LOC728519; LOC654053; LOC649553; HSD17B8; C16orf30; GADD45G; TPST1; GNG7; SV2A; LOC649946; LOC100129697; RARRES3; C8orf83; TNFSF13B; SNRPD3; LOC645232; PI3; WDFY1; LOC100133678; BAMBI; POPS; TARBP1; IRAK3; ZNF7; NLRC4; SKAP1; GAS7; C12orf29; KLRD1; ABHD15; CCDC146; CASP5; AARS2; LOC642103; LOC730385; GAR1; MAF; ARAP2; C16orf7; HLA-C; FLJ22662; DACH1; CRY1; CRY1; LRRC25; KIAA0564; UPF3A; MARCO; SRPRB; MAD1L1; LOC653610; P4HTM; CCL4L1; LAPTM4B; MAPK14; CD96; TLR7; KCNMB1; P2RX7; LOC650140; LOC791120; LTF; C3orf75; GPX7; SPRYD5; MOV10; EEF1B2; CTDSPL; HIST2H2BE; SLC38A1; AIM2; LOC100130904; LOC650546; P2RY10; ILSRA; MMP8; LOC100128485; RPS23; HDAC7; GUCY1A3; TGFA; NAIP; NAIP; NELL2; SIDT1; SLAMF1; MAPK14; CCR3; MKNK1; D4S234E; NBN; LOC654346; FGFBP2; BTLA; LRRN3; MT2A; LOC728790; LOC646672; NTN3; CD8A; CD8A; ZBP1; LDOC1L; CHM; LOC440731; LOC100131787; TNFRSF10C; LOC651612; STX11; LOC100128060; C1QB; PVRL2; ZMYND15; TRAPPC2P1; SECTM1; TRAT1; CAMKK2; CXCR5; CD163; FAS; RPL12P6; LOC100134734; CD36; FCGR1B; NR3C2; CSGALNACT2; GATA2; EBI2; EBI2; FKBP5; CRISPLD2; LOC152195; LOC100132199; DGAT2; SCML1; LSS; CIITA; SAP30; TLR5; NAMPT; GZMK; CARD17; INCA; MSL3L1; CD8A; MIIP; SRPK1; SLC6A6; C10orf119; C17orf60; LOC642816; AKR1C3; LHFPL2; CR1; KIAA1026; CCDC91; FAM102A; FAM102A; UPRT; PLEKHA1; CACNA2D3; DDX10; RPL23A; C2orf44; LSP1; C7orf53; DNAJC5; SLAIN1; CDKN1C; HIATL1; CRELD1; ZNHIT6; TIFA; ARL4C; PIGU; MEF2A; PIK3CB; CDK5RAP2; FLNB; GRAP; BATF; CYP4F3; KIR2DL3; C19orf59; NRG1; PPP2R2B; CDK5RAP2; PLSCR1; UBL7; HES4; ZNF256; DKFZp761E198; SAMD14; BAG3; PARP14; MS4A7; ECHDC3; OCIAD2; LOC90925; RGL4; PARP9; PARP9; CD151; SAAL1; LOC388076; SIGLEC5; LRIG1; PTGDR; PTGDR; NBPF8; NHS; ACSL1; HK3; SNX20; F2RL1; F2RL1; PARP12; LOC441506; MFGE8; SERPINA10; FAM69A; IL4R; KIAA1671; OAS3; PRR5; TMEM194; MS4A1; MTHFD2; LOC400793; CEACAM1; APP; RRBP1; SLCO4C1; XAF1; XAF1; SLC2A6; ZNF831; ZNF831; POLR1C; GLT1D1; VDR; IFIT5; SNHG8; TOP1MT; UPP1; SYTL2; LOC440359; KLRB1; MTMR3; S1PR1; FYB; CDC20; MEX3C; FAM168B; SLC4A7; CD79B; FAM84B; LOC100134688; LOC651738; PLAGL1; TIMM10; LOC641710; TRAF5; TAP1; FCRL2; SRC; RALGAPA1; OCIAD2; PON2; LOC730029; LOC100134768; LOC100134241; LOC26010; PLA2G12A; BACH1; DSC1; NOB1; LOC645693; LOC643313; BTBD11; REPS2; ZNF23; C18orf55; APOL2; APOL2; PASK; FER1L3; U2AF1; LOC285359; SIGLEC14; ARL1; C19orf62; NCR3; HOXB2; RNF135; IFIT1; KLF12; LILRB2; LOC728835; GSN; LOC100008589; LOC100008589; FLJ14213; SH2D3C; LOC100133177; HIST2H2AB; KIAA1618; C21orf2; CREB5; FAS; MTF1; RSAD2; ANPEP; C14orf179; TXNL4B; MYL9; MYL9; LOC100130828; LOC391019; ITGA2B; KLRC3; RASGRP2; NDST1; LOC388344; IF16; OAS1; OAS1; TRIM10; LIMK2; LIMK2; ATP5S; SMARCD3; PHC2; SOX8; LCK; SAMD9L; EHBP1; E2F2; CEACAM6; LOC100132394; LOC728014; LOC728014; SIRPG; OPLAH; FTHL2; CXorf21; CACNG6; C11orf75; LY9; LILRB4; STAT2; RAB20; SOCS1; PLOD2; UGDH; MAK16; ITGB3; DHRS9; PLEKHF1; ASAP1IT1; PSME2; LOC100128269; ALX1; BAK1; XPO4; CD247; FAM43A; ICOS; ISG15; HIST2H2AA4; CD79A; SLC25A4; TMEM158; GPR18; LAP3; TNFSF13B; TC2N; HSF2; CD7; C20orf3; HLA-DRB3; SESN1; LOC347376; P2RY14; P2RY14; P2RY14; CYP1B1; IFIT3; IFIT3; RPL13L; LOC729423; DBN1; TTC27; DPH5; GPR141; RBBP8; LOC654350; SLC30A1; PRSS23; JAM3; GNPDA2; IL7R; ACAD11; LOC642788; ALPK1; LOC439949; BCAT1; ATPGD1; TREML1; PECR; SPATA13; MAN1C1; ID01; TSEN54; SCRN1; LOC441193; LOC202134; KIAA0319L; MOSC1; PFKFB3; GNB4; ANKRD22; PROS1; CD40LG; RIOK2; AFF1; HIST1H3D; SLC26A8; SLC26A8; RNASE3; UBE2L6; UBE2L6; SSH1; KRBA1; SLC25A23; DTX3L; DOK3; SULT1B1; RASGRP4; ALOX15B; ADM; LOC391825; LOC730234; HIST2H2AA3; HIST2H2AA3; LIMK2; MMRN1; FKBP1A; GYG1; ASF1A; CD248; CD3G; DEFA1; EPHX2; CST7; ABLIM3; ANKRD55; SLC45A3; RAB33B; LILRA6; LILRA6; SPTLC2; CDA; PGD; LOC100130769; ECHDC2; KIF20B; B3GNT8; PYHIN1; LBH; LBH; BPI; GAR1; ST3GAL4; TMEM19; DHRS12; DHRS12; FAM26F; FCRLA; OSBPL7; CTSB; ALDH1A1; SRRD; TOLLIP; ICAM1; LAX1; CASP7; ZDHHC19; LOC732371; DENND1A; EMR2; LOC643308; ADA; LOC646527; LOC643313; GZMB; OLIG2; HLA-DPB1; MX1; THOC3; TRPM6; GK; JAK2; ARHGEF11; ARHGEF11; HOMER2; TACSTD2; CA4; GAA; IFITM3; CLYBL; CLYBL; MME; ZNF408; STAT1; STAT1; PNPLA7; INDO; PDZD8; PDGFD; CTSL1; HOMER3; CEP78; SBK1; ALG9; IL1R2; RAB40B; MMP23B; PGLYRP1; UHRF1; IF144L; PARP10; PARP10; GOLGA8A; CCR7; HEMGN; TCF7; CLUAP1; LOC390735; LOC641849; TYMP; DEFA1B; DEFA1B; DEFA1B; REPS2; REPS2; OSBPL1A; C11orf1; MCTP2; EMR4; LOC653316; FCRL6; MRPS26; RHOBTB3; DIRC2; CD27; PLEKHG4; CDH6; C4orf23; HIST2H2AC; SLC7A6; SLC7A6; SLAMF6; RETN; FAIM3; TMEM99; LOC728411; TMEM194A; NAPEPLD; ACOX1; CTLA4; SCO2; STK3; FLT3LG; VASP; FBXO31; TDRD9; TDRD9; LOC646144; NUSAP1; GPR97; GPR97; GPR97; EMR1; SLAMF6; CCDC106; ODF3B; LOC100129904; PADI4; LOC100132858; PIK3AP1; ZNF792; DIP2A; OSCAR; CLIC3; FANCE; TECPR2; P2RY10; ADORA3; IL18RAP; DEFA3; BRSK1; LOC647691; S1PR5; CPA3; BMX; DDX58; RHOBTB1; TNFRSF25; LOC730387; OLR1; HERC5; STAT1; NELF; STAP1; ZNF516; ARHGAP26; TIMP2; FCGR1A; RHOH; IF144; MTX3; CD74; LCK; TLR4; DSC2; CXorf45; ENPP4; CD300C; OASL; HPSE; MTHFD2; GSTM2; OLFM4; ABHD12B; LOC728417; LOC728417; FCAR; GTPBP3; KLF4; HOPX; THBD; HIST1H2BG; LOC730995; NOP56; ZBTB9; NLRC3; LOC100134083; COP1; CARD16; SP140; CD96; POLD2; IL32; LOC728744; FZD2; ZAP70; PYHIN1; SCARF1; IF127; PFKFB2; PAM; WARS; TCN1; LOC649839; MMP9; TMEM194A; TAP2; C17orf87; LOC728650; PNMA3; CPT1B; LTBP3; CCDC34; PRAGMIN; C9orf91; SMPDL3A; GPR56; C14orf147; SMARCD3; FAM119A; LOC642334; ENOSF1; FAR2; LOC441763; TESC; CECR6; KIAA1598; GPR109B; LRRN3; RNF213; LRP3; ASGR2; ASGR2; ZSCAN18; MCOLN2; IFIT2; PLCH2; MAP7; GBP4; MGMT; GAL3ST4; C2orf89; TXNDC3; IFIH1; PRRG4; LOC641693; LOC728093; TNFAIP8L1; AP3M2; BACH2; BACH2; C9orf123; CACNA1I; LOC100132287; CAMK1D; ANKRD33; CCR6; ALDH1A1; LOC100132797; CD163; ESAM; FCAR; TCN2; CD6; CD3E; CCDC76; MS4A1; IFIT1; MED13L; SLC26A8; NOV; FLJ20035; UGT1A3; LOC653600; LOC642684; KIAA0319L; KLRD1; TRIM22; C4orf18; TSPAN3; TSPAN3; DNAJC3; AGTRAP; LOC646786; NCALD; TTC25; TSPAN5; ZNF559; NFKB2; LOC652616; HLA-DOA; WARS; GBP2; AUTS2; IGF2BP3; OASL; DYSF; FLJ43093; MS4A14; TGFB1I1; RAD51C; CALD1; LOC730281; MUC1; C14orf124; RPL14; APOL6; KCTD12; ITGAX; IFIT3; LPCAT2; ZNF529; AGTRAP; LOC402112; LOC100134822; SH2D1B; MPO; LOC100131967; LOC440459; FAM44B; ACOT9; LOC729915; PDZK1IP1; S100A12; RAB3IL1; TMEM204; CXCL10; TSR1; MXD3; LILRA5; CKAP4; C6orf190; ECGF1; LDLRAP1; GRB10; FCRL3; LOC731275; ZFP91; CTRL; BCL6; SAMD3; LOC647436; CLC; GK; LOC100133565; OAS2; LOC644937; SIRPD; GPBAR1; GNL3; CD79B; ELF2; GAA; CD47; NMT2; MATR3; TMEM107; GCM1; RORA; MGAM; LOC100132491; KRT72; SEPT04; ACADVL; ANXA3; MEGF9; MEGF9; PTPRJ; HLA-DRB4; FFAR2; PML; HLA-DQA1; CEACAM8; SH3KBP1; TRPM2; CUX1; LOC648390; SUV39H1; USF1; VAPA; ALOX15; CD79A; DPRXP4; LOC652750; ECM1; ST6GAL1; KLHL3; RTP4; FAM179A; HDC; SACS; C9orf72; C9orf72; LOC652726; PVRIG; PPP1R16B; NSUN7; NSUN7; ZNF783; LOC441013; LOC100129343; OSM; UNC93B1; DNAJC30; FLJ14166; C9orf72; SAMD4A; F5; PARP15; PAFAH2; COL17A1; TYMP; LOC389672; ABCB1; LOC644852; TARP; SLAMF7; FRMD3; LOC648984; PLAUR; LOC100132119; KLRG1; INTS2; MYC; HIST1H4H; C9orf45; GBP6; KIFAP3; HSPC159; SOCS3; GOLGA8B; LOC100133583; ARL4A; ASNS; ITGAX; LOC153561; GSTM1; OAS2; OAS2; TRIM25; ABHD14A; LOC642342; GPR56; C4orf18; AK1; PIK3R6; HSPE1; ASPHD2; DHRS9; GRN; BOAT; LOC100134300; SDSL; TNFAIP6; LOC402176; LOC441019; FAM134B; ZNF573, GGGGTAACACAGAGTGCCCTTATGAAGGAGTTGGAGATCCTgcaaggaag (SEQ ID NO.:69); AAACCCGTCACCCAGATCGTCAGCGCCGAGGCCTGGGGTAGAGCAGGTGA (SEQ ID NO.:87); TGTTCTTCCCCATGTCCTGGATGCCACTGGAAGTGCACACTGCTTGTATG (SEQ ID NO.:93); CCCTGGAAAGCTCCCCGACAACCTCCACTGCCATTACCCACTAGGCAAGT (SEQ ID NO.:95); CCTCCAGTGGTTTAGGCAGGACCCTGGGAAAGGTCTCACATCTCTGTTGC (SEQ ID NO.:174); GCACCATGCATGGAGTCAGCCATTTCTCTAGGAACCTTGATTCCTGTCTG (SEQ ID NO.:193); CCCCACGCCTGTTTGTATTGGGAGCTCTGGACCAATAGTGTCTCTCCTAG (SEQ ID NO.:196); CCAGCCACTCTACTCAAGGGGCATATATTTTGGCATGAGGTGGGATAGAG (SEQ ID NO.:240); gcatgtgtatgatgtgtgtgcgtcggaccgcttctaggctactaagtgtc (SEQ ID NO.:257); AGGGGCAGTATACTCTTATCAGTGCGAGGTAGCTGGGGCCTGTGATAGTT (SEQ ID NO.:299); CAAGCCTGGCAGTAAATCCGAATATCCAGAACCCTGACCCTGCCGTGTAC (SEQ ID NO.:319); CAGCATGTAGGGCAGTGCTTGCACGTAGCATCTGGTGCCTAACCAGTGTT (SEQ ID NO.:336); CTGAGGTTATGTACAACCAACTCTCAGAATTCAGACTTCCTGCAGCTGCC (SEQ ID NO.:370); GTAGGCCCCCAAAGTGCCGTCTTTCCCTAGCATTTTACTCAATGTTTGCC (SEQ ID NO.:392); GAATCAAGGAGGTCAAGTAAGGTCACAGGGGCACTTGGGTTGAGCCAGGG (SEQ ID NO.:437); CCCCAGATGGTTCCAAATATTCCTTACCTCGTTTGGTTCCCAAGTCACAG (SEQ ID NO.:450); GAATAGAAACCAGACAGCAATTCTTTAGTTCCAGCCACCATTCGCCCCAC (SEQ ID NO.:454); TCAACAAAGAGGTGCTGACCTGAGAGTAGGGCACATAACCTCAGCCACTG (SEQ ID NO.:471); ATGTAGATGGGGAGTGACCACCGCCAACAGAAGTGTGGCCATCTTGCCCG (SEQ ID NO.:535); CTTTGGGCACCATTTGGATATAGTTAGTGGTGGTTTAGCTATGGCGTTCC (SEQ ID NO.:609); GGCAAATTCCGGGTATGCACTCAACTTCGGCAAAGGCACCTCGCTGTTGG (SEQ ID NO.:637); GAGGCTTTCAGGTAGGAGGACAATGGTAGCACTGTAGGTCCCCAGTGTCG (SEQ ID NO.:754); AGTAAACCCATATATCCAGAACCCTGACCCTGCCGTGTACCAGCTGAGAG (SEQ ID NO.:800); CCTGTGGCAAGCCAGCAAGATGGCCCTGGTGACAGCAAAAGAAACTGCAC (SEQ ID NO.:837); CCAGGTGCCGCCCACTCTTGACGTGATACTTACCGTCAATGCTCCTTACC (SEQ ID NO.:876); GCCTAAACCAGGTATGCCAATCTGTCTTGTGTCCACATACTAACAGAGGG (SEQ ID NO.:924); AGCCAAGACAGCAGCTCTACATCCTTACCTAGGTAATTCAGGCATGCGCC (SEQ ID NO.:947); CACATGGCAAATGCCTCCTTTCACAATAGAGCATGGTGCTGTTTCCTCAC (SEQ ID NO.:954); TATTGCAGCCATCCATCTTGGGGGCTCATCCATCACACCCGGGTTGCTAG (SEQ ID NO.:1010); CTGGGCTGTGGTATTTGGGTGATCTTTACATTCTTCAGACTCATGTGTGT (SEQ ID NO.:1035); GCTACAAACAAGCTCATCTTTGGAACTGGCACTCTGCTTGCTGTCCAGCC (SEQ ID NO.:1081); CCTACTCCTACAGTGCCTTGCATTCCGTAGCTGCTCAGTACATTAACCCA (SEQ ID NO.:1116); CAGGGTATGAAAGTGCCCATTTCTAGCCAACATTAGATACCCTCAGTCTC (SEQ ID NO.:1157); TGGCCACATTTGTCTCAAACTCAAGTCTACACATTTCTCTCTCTTTTCCC (SEQ ID NO.:1227); GTACCGTCAGCAACCTGGACAGAGCCTGACACTGATCGCAACTGCAAATC (SEQ ID NO.:1276); and Gccccctaattgactgaatggaacccctcttgaccaaagtgaccccagaa (SEQ ID NO.:1379). In another aspect, the method further comprises the step of differentiating between sarcoidosis and tuberculosis, lung cancer or pneumonia by determining the expression levels of the following genes, markers, or probes: PHF20L1; LOC400304; SELM; DPM2; RPLP1; SF1; ZNF683; CTTN; PTCRA; SNORA28; RPGRIP1; GPR160; PPIA; DNASE1L1; HEMGN; RAB13; NFIA; LOC728843; LOC100134660; LOC100132564; HIP1; PRMT1; PDGFC; NCRNA00085; NFATC3; GIMAP7; LOC100130905; AKAP7; TLE3; NRSN2; RPL37; CSTA; C20orf107; TMEM169; GCAT; TMEM176A; CMTM5; C3orf26; FANCD2; C9orf114; TIAM2; LOC644615; PADI2; GRINA; CHST13; ANGPT1; KIF27; ZNF550; PIK3C2A; NR1H3; ALG8; SLC2A5; ITGB5; OPN3; UBE2O; RIN3; LOC100129203; B3GNT1; NEK8; SLC38A5; GPR183; LOC728748; LOC646966; FAM159A; LOC441073; CCNC; MRPL9; SLC37A1; NSUN5; GHRL; ALAS2; MPZL2; RNF13; SUMO1P1; UHRF2; RNY4; LOC651524; KBTBD8; ZNF224; OLIG1; TNFRSF4; BEND7; LOC728323; ARHGAP24; CCCTGCCCTCATGTTGCTTTGGGTCTAGTGGAGGAGAGAGACAGATAAGC (SEQ ID NO.:1447); CAAGTTCTTAACCATCCCGGGTTCCAGTGGTTACAGAGTTCTGCCCTGGG; (SEQ ID NO.:1448) and TGCATGAGATCACACAACTAGGCGGTGACTGAGTCCAACACACCAAAGCC (SEQ ID NO.:1449). In another aspect, the method further comprises the step of differentiating between sarcoidosis that is active and sarcoidosis that is inactive by determining the expression levels of the following genes, markers, or probes: LOC442132; HOXA1; LOC652102; PPIE; C22orf27; TEX10; LMTK2; LOC283663; SUCNR1; COLQ; HLA-DOB; SAMSN1; INPP5E; CYP4F3; CRYZ; CDC14A; LOC653061; KIR2DL4; PCYOX1L; TCEAL3; FRRS1; PHF17; PDK4; LOC440313; ZNF260; SLFN13; VASH1; GM2A; ASAP2; VARS2; RPL14; KIR2DL1; SBDSP; S1PR3; and METTL1; CCAGGAGGCCGAACACTTCTTTCTGCTTTCTTGACATCCGCTCACCAGGC (SEQ ID NO.:1450), and TTCCAGGGCACGAGTTCGAGGCCAGCCTGGTCCACATGGGTCGGaaaaaa (SEQ ID NO.:1451). In another aspect, the method further comprises the step of using 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 144, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1,000, 1,100, 1,200, 1,300, 1,400, or 1,446 genes selected from SEQ ID NOS.: 1 to 1446 to determine if the patient has at least one of tuberculosis, sarcoidosis, cancer or pneumonia.

Yet another embodiment of the present invention includes a method for determining the effectiveness of a treating a sarcoidosis patient comprising: obtaining a sample from a subject suspected of having a pulmonary disease; determining the expression level of 3, 4, 5, 6 or more genes selected from IL1R2; GRB10; CEACAM4; SIPA1L2; BMX; IL1RAP; REPS2; ANXA3; MMP9; PHC2; HAUS4; DUSP1; CA4; SAMSN1; KLHL2; ACSL1; NSUN7; IL18RAP; GNG10; SMAP2; MGAM; LIN7A; IRAK3; USP10; CEBPD; TGFA; FOS; MANSC1; SLC26A8; ROPN1L; GPR97; NAMPT; MRVI1; KCNJ15; KLHL8; GNG10; MEGF9; GPR160; B4GALT5; STEAP4; LRG1; F5; PHTF1; HMGB2; DGAT2; SLC11A1; QPCT; PANX2; GPR141; or LMNB1; wherein overexpression of the genes is indicative of a reduction in sarcoidosis.

Another embodiment of the present invention includes a method of identifying a subject with a pulmonary disease comprising: obtaining a sample from a subject suspected of having a pulmonary disease; determining the expression level of six or more genes from each of the following genes selected from: UBE2J2; ALPL; JMJD6; FCER1G; LILRA5; LY96; FCGR1C; C10orf33; GPR109B; PROK2; PIM3; SH3GLB1; DUSP3; PPAP2C; SLPI; MCTP1; KIF1B; FLJ32255; BAGE5; IFITM1; GPR109A; IF135; LOC653591; KREMEN1; IL18R1; CACNA1E; ABCA2; CEACAM1; MXD4; TncRNA; LMNB1; H2AFJ; HP; ZNF438; FCER1A; SLC22A4; DISC1; MEFV; ABCA1; ITPRIPL2; KCNJ15; LOC728519; ERLIN1; NLRC4; B4GALT5; LOC653610; HIST2H2BE; AIM2; P2RY10; CCR3; EMR4P; NTN3; C1QB; TAOK1; FCGR1B; GATA2; FKBP5; DGAT2; TLR5; CARD17; INCA; MSL3L1; ESPN; LOC645159; C19orf59; CDK5RAP2; PLSCR1; RGL4; IFI30; LOC641710; GAGGCTTTCAGGTAGGAGGACAATGGTAGCACTGTAGGTCCCCAGTGTCG (SEQ ID NO.: 754); LOC100008589; LOC100008589; SMARCD3; NGFRAP1; LOC100132394; OPLAH; CACNG6; LILRB4; HIST2H2AA4; CYP1B1; PGS1; SPATA13; PFKFB3; HIST1H3D; SNORA73B; SLC26A8; SULT1B1; ADM; HIST2H2AA3; HIST2H2AA3; GYG1; CST7; EMR4; LILRA6; MEF2D; IFITM3; MSL3; DHRS13; EMR4; C16orf57; HIST2H2AC; EEF1D; TDRD9; GPR97; ZNF792; LOC100134364; SRGAP3; FCGR1A; HPSE; LOC728417; LOC728417; MIR21; HIST1H2BG; COP1; SMARCD3; LOC441763; ZSCAN18; GNG8; MTRF1L; ANKRD33; PLAC8; PLAC8; SLC26A8; AGTRAP; FLJ43093; LPCAT2; AGTRAP; S100A12; SVIL; LILRA5; LILRA5; ZFP91; CLC; LOC100133565; LTB4R; SEPT04; ANXA3; BHLHB2; IL4R; IFNAR1; MAZ; gccccctaattgactgaatggaacccctcttgaccaaagtgaccccagaa (SEQ ID NO.: 1379); comparing the expression level of the 3, 4, 5, 6 or more genes with the expression level of the same genes from individuals not afflicted with a pulmonary disease, and determining the level of expression of the six or more genes in the sample from the subject relative to the samples from individuals not afflicted with a pulmonary disease for the genes expressed in the one or more expression pathways, selected from: EIF2 signaling and mTOR signaling pathways are indicative of active sarcoidosis; co-expression of genes in the regulation of eIF4 and p70s6K signaling pathways is indicative of pneumonia; co-expression of genes in the interferon signaling and antigen presentation pathways are indicative of tuberculosis; and co-expression of genes in the T cell signaling pathways; and other signaling pathways is indicative of lung cancer. In one aspect, the genes that are downregulated are selected from MEF2D; BHLHB2; CLC; FCER1A; SRGAP3; FLJ43093; CCR3; EMR4; ZNF792; C10orf33; CACNG6; P2RY10; GATA2; EMR4P; ESPN; EMR4; MXD4; and ZSCAN18. In another aspect, the method further comprises a method for displaying if the patient has tuberculosis, sarcoidosis, cancer or pneumonia by aggregating the expression data from the six or more genes into a single visual display of a vector of expression for tuberculosis, sarcoidosis, cancer or pneumonia. In another aspect, the method further comprises the step of detecting and evaluating 7, 8, 9, 10, 12, 15, 20, 25, 35, 50, 75, 90, 100, 125, or 144 genes for the analysis. In another aspect, the sample is a blood, peripheral blood mononuclear cells, sputum, or lung biopsy. In another aspect, the expression level comprises an mRNA expression level and is quantitated by a method selected from the group consisting of polymerase chain reaction, real time polymerase chain reaction, reverse transcriptase polymerase chain reaction, hybridization, probe hybridization and gene expression array. In another aspect, the expression level is determined using at least one technique selected from polymerase chain reaction, heteroduplex analysis, single stand conformational polymorphism analysis, ligase chain reaction, comparative genome hybridization, Southern blotting, Northern blotting, Western blotting, enzyme-linked immunosorbent assay, fluorescent resonance energy-transfer and sequencing. In another aspect, the expression level is determined by microarray analysis that comprises use of oligonucleotides that hybridize to mRNA transcripts or cDNAs for the six or more genes, and wherein the oligonucleotides are disposed or directly synthesized on the surface of a chip or wafer. In another aspect, the oligonucleotides are about 10 to about 50 nucleotides in length. In another aspect, the method further comprises the step of using the determined comparative gene product information to formulate at least one of diagnosis, a prognosis or a treatment plan. In another aspect, the patient's disease state is further determined by radiological analysis of the patient's lungs. In another aspect, the method further comprises step of determining a treated patient gene expression dataset after the patient has been treated and determining if the treated patient gene expression dataset has returned to a normal gene or a changed gene expression dataset thereby determining if the patient has been treated. In another aspect, a non-overlapping set of genes is used to distinguish between Tb, sarcoidosis, pneumonia and lung cancer, versus, Tb, active sarcoidosis, non-active sarcoidosis, pneumonia and lung cancer are selected from Table 11, 12 or both. Yet another embodiment of the present invention includes a computer readable medium comprising computer-executable instructions for performing the methods of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the features and advantages of the present invention, reference is now made to the detailed description of the invention along with the accompanying figures and in which:

FIG. 1 shows a heatmap of pulmonary granulomatous diseases, TB and sarcoidosis, display similar transcriptional signatures (of 1446 transcripts) to each other but distinct from pneumonia and lung cancer.

FIG. 2 shows a heat map with three dominant clusters of transcripts in the unsupervised clustering of the 1446 transcripts are associated with distinct Ingenuity Pathway Analysis canonical pathways.

FIGS. 3A and 3B (quantitative) show that sarcoidosis patients clinically classified as active sarcoidosis display similar transcriptional signatures to the TB patients but are very distinct from the transcriptional signatures of the clinically classified non-active sarcoidosis patients, which in turn resemble the healthy controls.

FIGS. 4A to 4E show a modular analysis of the Training Set shows the similarity of the biological pathways associated with TB and sarcoidosis (which show particularly overexpression of the IFN modules), differing from pneumonia and lung cancer (particularly overexpression of the inflammation modules). All are quantitated in FIGS. 4D and 4E

FIGS. 5A to 5E show a Comparison Ingenuity Pathway Analysis of the four disease groups compared to their matched controls reveals the four most significant pathways.

FIGS. 6A to 6D shows both modular analysis and molecular distance to health reveal that the blood transcriptome of the pneumonia and TB patients after successfully completing treatment are no different from the healthy controls, however the sarcoidosis patients show an overexpression of inflammation genes during a clinically successful response to glucocorticoids.

FIGS. 7A to 7E shows that the Interferon-inducible gene expression is most abundant in the neutrophils in both TB and sarcoidosis.

FIGS. 8A and 8B are graphs with the results for the pulmonary diseases using the genes in the neutrophil module.

FIG. 9 is a 4-set Venn diagram comparing the differentially expressed genes for each disease group compared to their ethnicity and gender matched controls.

FIG. 10A is a Venn diagram comparing the gene lists used in the class prediction. FIG. 10B is a Venn diagram comparing the genes that distinguish between Tb, sarcoidosis, pneumonia and lung cancer, versus, Tb, active sarcoidosis, non-active sarcoidosis, pneumonia and lung cancer.

DETAILED DESCRIPTION OF THE INVENTION

While the making and using of various embodiments of the present invention are discussed in detail below, it should be appreciated that the present invention provides many applicable inventive concepts that can be embodied in a wide variety of specific contexts. The specific embodiments discussed herein are merely illustrative of specific ways to make and use the invention and do not delimit the scope of the invention.

To facilitate the understanding of this invention, a number of terms are defined below. Terms defined herein have meanings as commonly understood by a person of ordinary skill in the areas relevant to the present invention. Terms such as “a”, “an” and “the” are not intended to refer to only a singular entity, but include the general class of which a specific example may be used for illustration. The terminology herein is used to describe specific embodiments of the invention, but their usage does not delimit the invention, except as outlined in the claims.

The present invention provides methods, compositions, biomarkers and tests for evaluating the immunopathogenesis underlying TB and other pulmonary diseases, by comparing the blood transcriptional responses in pulmonary TB patients to that found in pulmonary sarcoidosis, pneumonia and lung cancer patients. It also provides for the first time a complete, reproducible comparison of blood transcriptional responses before and after treatment in each disease, and examining the transcriptional responses seen in the different leucocyte populations of the granulomatous diseases. In addition the present inventors investigated the association between the clinical heterogeneity of sarcoidosis and the observed blood transcriptional heterogeneity.

As used herein, the term “array” refers to a solid support or substrate with one or more peptides or nucleic acid probes attached to the support. Arrays typically have one or more different nucleic acid or peptide probes that are coupled to a surface of a substrate in different, known locations. These arrays, also described as “microarrays” or “gene-chips” that may have 10,000; 20,000, 30,000; or 40,000 different identifiable genes based on the known genome, e.g., the human genome. These pan-arrays are used to detect the entire “transcriptome” or transcriptional pool of genes that are expressed or found in a sample, e.g., nucleic acids that are expressed as RNA, mRNA and the like that may be subjected to RT and/or RT-PCR to made a complementary set of DNA replicons. The microarray is well known in the art, for example, U.S. Pat. Nos. 5,445,934 and 5,744,305. The term also includes all the devices so called in Schena (ed.), DNA Microarrays: A Practical Approach (Practical Approach Series), Oxford University Press (1999) (ISBN: 0199637768); Nature Genet. 21(1)(suppl):1-60 (1999); and Schena (ed.), Microarray Biochip: Tools and Technology, Eaton Publishing Company/BioTechniques Books Division (2000) (ISBN: 1881299376)(relevant portions incorporated herein by reference), the disclosures of which are incorporated herein by reference in their entirety. Arrays may be produced using mechanical synthesis methods, light directed synthesis methods and the like that incorporate a combination of non-lithographic and/or photolithographic methods and solid phase synthesis methods. In one embodiment, the present invention includes simplified arrays that can include a limited number of probes, e.g., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 144, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1,000, 1,100, 1,200, 1,300, 1,400, or even 1,446 genes or probes in a customized or customizable microarray adapted for pulmonary disease detection, diagnosis and evaluation.

As used herein the term “biomarker” refers to a specific biochemical in the body that has a particular molecular feature to make it useful for diagnosing and measuring the progress of disease or the effects of treatment. Certain biomarkers form part of the present invention and are attached to this application as Lengthy Tables, that are included herewith and the content incorporated herein by reference. The text file Symbol-Regulation-ID.txt is 47Kb and Symbol-Sequence-ID.txt provide the list of 1446 probe sequences and genes that are associated with the majority of the same. Also included herewith is a list of 1359 genes that overlay in certain conditions as described hereinbelow.

Various techniques for the synthesis of these nucleic acid arrays have been described, e.g., fabricated on a surface of virtually any shape or even a multiplicity of surfaces. Arrays may be peptides or nucleic acids on beads, gels, polymeric surfaces, fibers such as fiber optics, glass or any other appropriate substrate. Arrays may be packaged in such a manner as to allow for diagnostics or other manipulation of an all inclusive device, see for example, U.S. Pat. No. 6,955,788, relevant portions incorporated herein by reference.

As used herein, the term “disease” refers to a physiological state of an organism with any abnormal biological state of a cell. Disease includes, but is not limited to, an interruption, cessation or disorder of cells, tissues, body functions, systems or organs that may be inherent, inherited, caused by an infection, caused by abnormal cell function, abnormal cell division and the like. A disease that leads to a “disease state” is generally detrimental to the biological system, that is, the host of the disease. With respect to the present invention, any biological state, such as an infection (e.g., viral, bacterial, fungal, helminthic, etc.), inflammation, autoinflammation, autoimmunity, anaphylaxis, allergies, premalignancy, malignancy, surgical, transplantation, physiological, and the like that is associated with a disease or disorder is considered to be a disease state. A pathological state is generally the equivalent of a disease state. Disease states may also be categorized into different levels of disease state. As used herein, the level of a disease or disease state is an arbitrary measure reflecting the progression of a disease or disease state as well as the physiological response upon, during and after treatment. Generally, a disease or disease state will progress through levels or stages, wherein the affects of the disease become increasingly severe. The level of a disease state may be impacted by the physiological state of cells in the sample. As used herein, the terms “module”, “modular transcriptional vectors”, or “vectors of gene expression” refer to transcriptional expression data that reflects a proportion of differentially expressed genes having a common gene expression pathway (e.g., interferon inducible genes), are typically expressed only or predominantly in a certain cell type (e.g., genes expressed by neutrophils), or are grouped into a module of genes to yield, in the aggregate a single vector of gene expression, such that the overall expression is expressed as a single vector that includes both a direction (under expressed or over expressed) and intensity of the under or over expression. For example, for each module the proportion of transcripts differentially expressed between at least two groups (e.g., healthy subjects versus patients, or certain patients of a first disease versus a group of patients with a second disease). The vector of expression is derived from the comparison of two or more groups of samples. The first analytical step is used for the selection of disease-specific sets of transcripts within each module. Next, there is the “expression level.” The group comparison for a given disease provides the list of differentially expressed transcripts for each module. It was found that different diseases yield different subsets of modular transcripts. With this expression level it is then possible to calculate a vector of expression for each of the module(s) for a single sample by averaging expression values of disease-specific subsets of genes identified as being differentially expressed. This approach permits the generation of maps of modular expression vectors for a single sample, e.g., those described in the module maps disclosed herein. These vector of expression or module maps represent an averaged expression level for each module (instead of a proportion of differentially expressed genes) that can be derived for each sample. An example of the vector of gene expression is shown in, e.g., FIG. 6A.

Using the present invention it is possible to identify and distinguish pulmonary diseases not only at the module-level, but also at the gene-level; i.e., two, three or four diseases can have for certain modules the same vector (identical proportion of differentially expressed transcripts, identical “polarity”), but the gene composition of the vector can still be disease-specific, and vice versa. Gene-level expression provides the distinct advantage of greatly increasing the resolution of the analysis.

Gene expression monitoring systems for use with the present invention may include customized gene arrays with a limited and/or basic number of genes that are specific and/or customized for the one or more target diseases. Unlike the general, pan-genome arrays that are in customary use, the present invention provides for not only the use of these general pan-arrays for retrospective gene and genome analysis without the need to use a specific platform, but more importantly, it provides for the development of customized arrays that provide an optimal gene set for analysis without the need for the thousands of other, non-relevant genes. One distinct advantage of the optimized arrays and modules of the present invention over the existing art is a reduction in the financial costs (e.g., cost per assay, materials, equipment, time, personnel, training, etc.), and more importantly, the environmental cost of manufacturing pan-arrays where the vast majority of the data is irrelevant. The modules of the present invention allow for the first time the design of simple, custom arrays that provide optimal data with the least number of probes while maximizing the signal to noise ratio. By eliminating the total number of genes for analysis, it is possible to, e.g., eliminate the need to manufacture thousands of expensive platinum masks for photolithography during the manufacture of pan-genetic chips that provide vast amounts of irrelevant data. Using the present invention it is possible to completely avoid the need for microarrays if the limited probe set(s) of the present invention are used with, e.g., digital optical chemistry arrays, ball bead arrays, beads (e.g., Luminex), multiplex PCR, quantitiative PCR, run-on assays, Northern blot analysis, or even, for protein analysis, e.g., Western blot analysis, 2-D and 3-D gel protein expression, MALDI, MALDI-TOF, fluorescence activated cell sorting (FACS) (cell surface or intracellular), enzyme linked immunosorbent assays (ELISA), chemiluminescence studies, enzymatic assays, proliferation studies or any other method, apparatus and system for the determination and/or analysis of gene expression that are readily commercially available.

As used herein, the term “differentially expressed” refers to the measurement of a cellular constituent (e.g., nucleic acid, protein, enzymatic activity and the like) that varies in two or more samples, e.g., between a disease sample and a normal sample. The cellular constituent may be on or off (present or absent), upregulated relative to a reference or downregulated relative to the reference. For use with gene-chips or gene-arrays, differential gene expression of nucleic acids, e.g., mRNA or other RNAs (miRNA, siRNA, hnRNA, rRNA, tRNA, etc.) may be used to distinguish between cell types or nucleic acids. Most commonly, the measurement of the transcriptional state of a cell is accomplished by quantitative reverse transcriptase (RT) and/or quantitative reverse transcriptase-polymerase chain reaction (RT-PCR), genomic expression analysis, post-translational analysis, modifications to genomic DNA, translocations, in situ hybridization and the like.

As used herein, the terms “therapy” or “therapeutic regimen” refer to those medical steps taken to alleviate or alter a disease state, e.g., a course of treatment intended to reduce or eliminate the affects or symptoms of a disease using pharmacological, surgical, dietary and/or other techniques. A therapeutic regimen may include a prescribed dosage of one or more drugs or surgery. Therapies will most often be beneficial and reduce the disease state but in many instances the effect of a therapy will have non-desirable or side-effects. The effect of therapy will also be impacted by the physiological state of the host, e.g., age, gender, genetics, weight, other disease conditions, etc.

As used herein, the term “pharmacological state” or “pharmacological status” refers to those samples from diseased individuals that will be, are and/or were treated with one or more drugs, surgery and the like that may affect the pharmacological state of one or more nucleic acids in a sample, e.g., newly transcribed, stabilized and/or destabilized as a result of the pharmacological intervention. The pharmacological state of a sample relates to changes in the biological status before, during and/or after drug treatment and may serve as a diagnostic or prognostic function, as taught herein. Some changes following drug treatment or surgery may be relevant to the disease state and/or may be unrelated side-effects of the therapy. Changes in the pharmacological state are the likely results of the duration of therapy, types and doses of drugs prescribed, degree of compliance with a given course of therapy, and/or un-prescribed drugs ingested.

As used herein, the term “biological state” refers to the state of the transcriptome (that is the entire collection of RNA transcripts) of the cellular sample isolated and purified for the analysis of changes in expression. The biological state reflects the physiological state of the cells in the blood sample by measuring the abundance and/or activity of cellular constituents, characterizing according to morphological phenotype or a combination of the methods for the detection of transcripts. As used herein, the term “expression profile” refers to the relative abundance of RNA, DNA abundances or activity levels. The expression profile can be a measurement for example of the transcriptional state or the translational state by any number of methods and using any of a number of gene-chips, gene arrays, beads, multiplex PCR, quantitiative PCR, run-on assays, Northern blot analysis, or using RNA-seq, nanostring, nanopore RNA sequencing etc. Apparatus and system for the determination and/or analysis of gene expression that are readily commercially available.

As used herein the term “gene” is used to refer to a functional protein, polypeptide or peptide-encoding unit. As will be understood by those in the art, this functional term includes both genomic sequences, cDNA sequences, or fragments or combinations thereof, as well as gene products, including those that may have been altered by the hand of man. Purified genes, nucleic acids, protein and the like are used to refer to these entities when identified and separated from at least one contaminating nucleic acid or protein with which it is ordinarily associated.

As used herein, the term “transcriptional state” of a sample includes the identities and relative abundances of the RNA species, especially mRNAs present in the sample. The entire transcriptional state of a sample, that is the combination of identity and abundance of RNA, is also referred to herein as the transcriptome. Generally, a substantial fraction of all the relative constituents of the entire set of RNA species in the sample are measured.

Regarding the “expression level,” the group comparison for a given disease provides the list of differentially expressed transcripts. It was found that different diseases yield different subsets of gene transcripts as demonstrated herein.

Gene expression monitoring systems for use with the present invention may include customized gene arrays with a limited and/or basic number of genes that are specific and/or customized for the one or more target diseases. Unlike the general, pan-genome arrays that are in customary use, the present invention provides for not only the use of these general pan-arrays for retrospective gene and genome analysis without the need to use a specific platform, but more importantly, it provides for the development of customized arrays that provide an optimal gene set for analysis without the need for the thousands of other, non-relevant genes. One distinct advantage of the optimized arrays and gene sets of the present invention over the existing art is a reduction in the financial costs (e.g., cost per assay, materials, equipment, time, personnel, training, etc.), and more importantly, the environmental cost of manufacturing pan-arrays where the vast majority of the data is irrelevant. By eliminating the total number of genes for analysis, it is possible to, e.g., eliminate the need to manufacture thousands of expensive platinum masks for photolithography during the manufacture of pan-genetic chips that provide vast amounts of irrelevant data. Using the present invention it is possible to completely avoid the need for microarrays if the limited probe set(s) of the present invention are used with, e.g., digital optical chemistry arrays, ball bead arrays, multiplex PCR, quantitiative PCR, “RNA-seq” for measuring mRNA levels using next-generation sequencing technologies, nanostring-type technologies or any other method, apparatus and system for the determination and/or analysis of gene expression that are readily commercially available.

The “molecular fingerprinting system” of the present invention may be used to facilitate and conduct a comparative analysis of expression in different cells or tissues, different subpopulations of the same cells or tissues, different physiological states of the same cells or tissue, different developmental stages of the same cells or tissue, or different cell populations of the same tissue against other diseases and/or normal cell controls. In some cases, the normal or wild-type expression data may be from samples analyzed at or about the same time or it may be expression data obtained or culled from existing gene array expression databases, e.g., public databases such as the NCBI Gene Expression Omnibus database.

As used herein, the term “differentially expressed” refers to the measurement of a cellular constituent (e.g., nucleic acid, protein, enzymatic activity and the like) that varies in two or more samples, e.g., between a disease sample and a normal sample. The cellular constituent may be on or off (present or absent), upregulated relative to a reference or downregulated relative to the reference. For use with gene-chips or gene-arrays, differential gene expression of nucleic acids, e.g., mRNA or other RNAs (miRNA, siRNA, hnRNA, rRNA, tRNA, etc.) may be used to distinguish between cell types or nucleic acids. Most commonly, the measurement of the transcriptional state of a cell is accomplished by quantitative reverse transcriptase (RT) and/or quantitative reverse transcriptase-polymerase chain reaction (RT-PCR), genomic expression analysis, post-translational analysis, modifications to genomic DNA, translocations, in situ hybridization and the like.

The skilled artisan will appreciate readily that samples may be obtained from a variety of sources including, e.g., single cells, a collection of cells, tissue, cell culture and the like. In certain cases, it may even be possible to isolate sufficient RNA from cells found in, e.g., urine, blood, saliva, tissue or biopsy samples and the like. In certain circumstances, enough cells and/or RNA may be obtained from: mucosal secretion, feces, tears, blood plasma, peritoneal fluid, interstitial fluid, intradural, cerebrospinal fluid, sweat or other bodily fluids. The nucleic acid source, e.g., from tissue or cell sources, may include a tissue biopsy sample, one or more sorted cell populations, cell culture, cell clones, transformed cells, biopies or a single cell. The tissue source may include, e.g., brain, liver, heart, kidney, lung, spleen, retina, bone, neural, lymph node, endocrine gland, reproductive organ, blood, nerve, vascular tissue, and olfactory epithelium.

The present invention includes the following basic components, which may be used alone or in combination, namely, one or more data mining algorithms, one novel algorithm specifically developed for this TB treatment monitoring, the Temporal Molecular Response; the characterization of blood leukocyte transcriptional gene sets; the use of aggregated gene transcripts in multivariate analyses for the molecular diagnostic/prognostic of human diseases; and/or visualization of transcriptional gene set-level data and results. Using the present invention it is also possible to develop and analyze composite transcriptional markers. The composite transcriptional markers for individual patients in the absence of control sample analysis may be further aggregated into a reduced multivariate score.

An explosion in data acquisition rates has spurred the development of mining tools and algorithms for the exploitation of microarray data and biomedical knowledge. Approaches aimed at uncovering the function of transcriptional systems constitute promising methods for the identification of robust molecular signatures of disease. Indeed, such analyses can transform the perception of large-scale transcriptional studies by taking the conceptualization of microarray data past the level of individual genes or lists of genes.

The present inventors have recognized that current microarray-based research is facing significant challenges with the analysis of data that are notoriously “noisy,” that is, data that is difficult to interpret and does not compare well across laboratories and platforms. A widely accepted approach for the analysis of microarray data begins with the identification of subsets of genes differentially expressed between study groups. Next, the users try subsequently to “make sense” out of resulting gene lists using the novel Temporal Molecular Response discovery algorithms and existing scientific knowledge and by validating in independent sample sets and in different microarray analyses.

Pulmonary tuberculosis (PTB) is a major and increasing cause of morbidity and mortality worldwide caused by Mycobacterium tuberculosis (M. tuberculosis). However, the majority of individuals infected with M. tuberculosis remain asymptomatic, retaining the infection in a latent form and it is thought that this latent state is maintained by an active immune response. Blood is the pipeline of the immune system, and as such is the ideal biologic material from which the health and immune status of an individual can be established.

Blood represents a reservoir and a migration compartment for cells of the innate and the adaptive immune systems, including neutrophils, dendritic cells and monocytes, or B and T lymphocytes, respectively, which during infection will have been exposed to infectious agents in the tissue. For this reason whole blood from infected individuals provides an accessible source of clinically relevant material where an unbiased molecular phenotype can be obtained using gene expression microarrays for the study of cancer in tissues autoimmunity), and inflammation, infectious disease, or in blood or tissue. Microarray analyses of gene expression in blood leucocytes have identified diagnostic and prognostic gene expression signatures, which have led to a better understanding of mechanisms of disease onset and responses to treatment. These microarray approaches have been attempted for the study of active and latent TB but as yet have yielded small numbers of differentially expressed genes only, and in relatively small numbers of patients, therefore not reaching statistical significance, which may not be robust enough to distinguish between other inflammatory and infectious diseases. The present inventors recognized that a neutrophil driven blood transcriptional signature in active TB patients was missing in the majority of Latent TB individuals and in healthy controls. For this description see, also, the study of Berry et al., 2010 (5), by the present inventors. This signature of active TB was reflective of lung radiographic disease and was diminished after 2 months of treatment (5) and more recently the present inventors have shown that the blood transcriptional signature of TB was diminished as early as 2 weeks after commencement of treatment (12). The signature was dominated by interferon-inducible genes, and at a modular level the active TB signature (5, 12) was distinct from other infectious or autoimmune diseases (5).

In the present findings and the basis of this application the blood transcriptional profiles of the pulmonary granulomatous diseases (TB and sarcoidosis) clustered together but distinctly from the similar pulmonary diseases pneumonia and lung cancer.

It has previously been shown that TB and sarcoidosis have similar transcriptional profiles however no published studies have determined if this similar blood gene expression profile is due to generalized transcriptional activity associated with pulmonary diseases or due to specific host responses associated with TB and sarcoidosis. Therefore, we recruited three cohorts of TB and sarcoidosis patients (Training, Test and Validation Sets) alongside patients with similar pulmonary diseases community acquired pneumonia and lung cancer. On average the sarcoidosis patients presented with a milder and more chronic presentation than the TB and pneumonia patients. There was little difference in the demographics and clinical characteristics of the participants in the Training and Test Sets.

Unbiased analysis followed by unsupervised hierarchical clustering of the blood transcriptional profiles from all the Training Set participants clearly demonstrated that the TB and sarcoidosis patients transcriptional profiles clustered together but distinctly from the pneumonia and cancer patients transcriptional profiles which themselves clustered together (3422 transcripts). Adding a statistical filter generated 1446 differentially expressed transcripts. Applying unsupervised hierarchical clustering of the 1446-transcripts and the Training Set samples again showed the same clustering pattern. This finding was verified in an independent cohort, the Test Set, which likewise showed the TB and most sarcoidosis patients clustered together while the pneumonia and lung cancer patients also clustered together but separately from the granulomatous diseases (FIG. 1). Clustering was not influenced by ethnicity or gender (data not shown).

FIG. 1. The pulmonary granulomatous diseases, TB and sarcoidosis, display similar transcriptional signatures to each other but distinct from pneumonia and lung cancer. 1446-transcripts were differentially expressed in the whole blood of the Training Set healthy controls, pulmonary TB patients, pulmonary sarcoidosis patients, pneumonia patients and lung cancer patients. The clustering of the 1446-transcripts were tested in an independent cohort from which they were derived from, the Test Set. The heatmap shows the transcripts and patients' profiles as organised by the unbiased algorithm of unsupervised hierarchical clustering. A dotted line is added to the heatmap to help visualisation of the main clusters generated by the clustering algorithm. Transcript intensity values are normalised to the median of all transcripts. Red transcripts are relatively over-abundant and blue transcripts under-abundant. The coloured bar at the bottom of the heatmap indicates which group the profile belongs to.

TABLE 1 List of 1446 genes that differentiate between lung cancer, pneumonia, TB and sarcodiosis. Cancer vs Pneumonia vs Sarcoidosis Tb vs SEQ Symbol Control Control vs Control Control ID NO: TMEM144 UP UP UP UP 1 FBLN5 DOWN DOWN DOWN DOWN 2 FBLN5 DOWN DOWN DOWN DOWN 3 ERI1 UP UP UP UP 4 CXCR3 DOWN DOWN DOWN DOWN 5 GLUL UP UP UP UP 6 LOC728728 UP UP UP UP 7 KLHDC8B UP UP UP UP 8 KCNJ15 UP UP UP UP 9 RNF125 DOWN DOWN DOWN DOWN 10 CCNB1IP1 DOWN DOWN DOWN DOWN 11 PSG9 UP UP UP UP 12 LOC100170939 UP UP UP UP 13 QPCT UP UP UP UP 14 CD177 UP UP UP UP 15 LOC400499 UP UP UP UP 16 LOC400499 UP UP UP UP 17 LOC100134634 UP UP UP UP 18 TMEM88 UP UP UP UP 19 LOC729028 UP UP DOWN UP 20 EPSTI1 UP UP UP UP 21 INSC UP UP UP UP 22 LOC728484 DOWN DOWN DOWN DOWN 23 ERP27 DOWN UP DOWN DOWN 24 CCDC109A UP UP UP UP 25 LOC729580 UP UP UP UP 26 C2 DOWN UP UP UP 27 TTRAP UP UP DOWN UP 28 ALPL UP UP DOWN UP 29 MAEA UP UP UP UP 30 COX10 DOWN DOWN DOWN DOWN 31 GPR84 UP UP UP UP 32 PHF20L1 UP UP UP UP 33 TRMT11 DOWN DOWN DOWN DOWN 34 ANKRD22 UP UP UP UP 35 MATK DOWN DOWN DOWN DOWN 36 TBC1D24 UP UP UP UP 37 LILRA5 UP UP UP UP 38 TMEM176B UP UP UP UP 39 CAMP UP UP UP UP 40 PKIA DOWN DOWN DOWN DOWN 41 PFTK1 UP UP UP UP 42 TPM2 DOWN DOWN DOWN DOWN 43 TPM2 DOWN DOWN DOWN DOWN 44 PRKCQ DOWN DOWN DOWN DOWN 45 PSTPIP2 UP UP UP UP 46 LOC129607 UP UP UP UP 47 APRT DOWN DOWN DOWN DOWN 48 VAMPS UP UP UP UP 49 FCGR1C UP UP UP UP 50 SHKBP1 UP UP UP UP 51 CD79B DOWN DOWN DOWN DOWN 52 SIGIRR DOWN DOWN DOWN DOWN 53 FKBP9L UP UP UP UP 54 LOC729660 UP UP UP UP 55 WDR74 DOWN DOWN DOWN DOWN 56 LOC646434 UP UP UP UP 57 LOC647834 UP UP DOWN UP 58 RECK DOWN DOWN DOWN DOWN 59 MGST1 UP UP UP UP 60 PIWIL4 UP UP UP UP 61 LILRB1 UP UP UP UP 62 FCGR1B UP UP UP UP 63 NOC3L DOWN DOWN DOWN DOWN 64 ZNF83 DOWN DOWN DOWN DOWN 65 FCGBP DOWN DOWN DOWN DOWN 66 SNORD13 DOWN DOWN DOWN DOWN 67 LOC642267 UP UP UP UP 68 UP UP UP UP 69 GBP5 DOWN UP UP UP 70 EOMES DOWN DOWN DOWN DOWN 71 BST1 UP UP UP UP 72 C5 UP UP UP UP 73 CHMP7 DOWN DOWN DOWN DOWN 74 ETV7 UP UP UP UP 75 LOC400304 DOWN DOWN DOWN DOWN 76 ILVBL DOWN DOWN DOWN DOWN 77 LOC728262 UP UP UP UP 78 GNLY DOWN DOWN DOWN DOWN 79 LOC388572 UP UP UP UP 80 GATA1 DOWN DOWN UP UP 81 MYBL1 DOWN DOWN DOWN DOWN 82 SELM DOWN DOWN DOWN DOWN 83 LOC441124 UP UP UP UP 84 LOC441124 UP UP UP UP 85 IL12RB1 DOWN DOWN UP UP 86 DOWN DOWN DOWN DOWN 87 BRIX1 DOWN DOWN DOWN DOWN 88 GAS6 DOWN UP UP UP 89 GAS6 UP UP UP UP 90 LOC100133740 UP UP UP UP 91 GPSM1 DOWN DOWN DOWN DOWN 92 DOWN UP UP UP 93 C6ORF129 DOWN DOWN DOWN DOWN 94 UP UP UP UP 95 IER3 UP UP UP UP 96 MAPK14 UP UP UP UP 97 PROK1 UP UP UP UP 98 GPR109B UP UP UP UP 99 SASP UP UP UP UP 100 LOC728093 UP UP UP UP 101 PROK2 UP UP DOWN UP 102 CTSW DOWN DOWN DOWN DOWN 103 ABHD2 UP UP UP UP 104 LOC100130775 DOWN DOWN DOWN DOWN 105 SLITRK4 UP UP UP UP 106 FBXW2 UP UP UP UP 107 RTTN DOWN DOWN DOWN DOWN 108 TAF15 UP UP DOWN DOWN 109 FUT7 UP UP UP UP 110 DUSP3 UP UP UP UP 111 LOC399715 UP UP DOWN UP 112 LOC642161 DOWN DOWN DOWN DOWN 113 LOC100129541 UP UP UP UP 114 TCTN1 DOWN DOWN DOWN DOWN 115 SLAMF8 DOWN UP UP UP 116 TGM2 DOWN DOWN DOWN DOWN 117 ECE1 UP UP UP UP 118 CD38 UP UP UP UP 119 INPP4B DOWN DOWN DOWN DOWN 120 ID3 DOWN DOWN DOWN DOWN 121 DPM2 DOWN DOWN UP DOWN 122 CR1 UP UP UP UP 123 CR1 UP UP UP UP 124 TAPBP DOWN UP UP UP 125 PPAP2C UP UP DOWN UP 126 MBOAT2 UP UP UP UP 127 MS4A2 DOWN DOWN UP DOWN 128 FAM176B UP UP UP UP 129 LOC390183 DOWN DOWN DOWN DOWN 130 RPLP1 DOWN DOWN DOWN DOWN 131 SERPING1 UP UP UP UP 132 LOC441743 DOWN DOWN DOWN DOWN 133 H1F0 UP UP UP UP 134 SOD2 UP UP DOWN UP 135 LOC642828 DOWN DOWN DOWN DOWN 136 POLB UP UP UP UP 137 TSPAN9 UP UP UP UP 138 ORMDL3 DOWN DOWN UP DOWN 139 FER1L3 UP UP UP UP 140 LBH DOWN DOWN DOWN DOWN 141 PNKD UP UP UP UP 142 SLPI UP UP DOWN UP 143 SIRPB1 UP UP UP UP 144 LOC389386 UP UP UP UP 145 REC8 UP UP UP UP 146 GNLY DOWN DOWN DOWN DOWN 147 GNLY DOWN DOWN DOWN DOWN 148 FOLR3 UP UP UP UP 149 LOC730286 UP UP UP UP 150 SKAP1 DOWN DOWN DOWN DOWN 151 SELP UP UP UP UP 152 DHX30 DOWN DOWN DOWN DOWN 153 KIAA1618 DOWN UP UP UP 154 NQO2 UP UP DOWN UP 155 SF1 UP DOWN DOWN UP 156 ANKRD46 DOWN DOWN DOWN DOWN 157 LOC646301 UP UP UP UP 158 LOC400464 DOWN DOWN DOWN DOWN 159 LOC100134703 UP UP UP UP 160 C20ORF106 UP UP UP UP 161 ZNF683 DOWN DOWN DOWN DOWN 162 SLC25A38 DOWN DOWN UP DOWN 163 YPEL1 DOWN DOWN DOWN DOWN 164 IL1R1 UP UP UP UP 165 EPHAl DOWN DOWN DOWN DOWN 166 CHD6 DOWN DOWN DOWN DOWN 167 LIMK2 UP UP UP UP 168 LOC643733 DOWN DOWN DOWN DOWN 169 LOC441550 DOWN DOWN DOWN DOWN 170 MGC3020 DOWN DOWN DOWN DOWN 171 ANKRD9 UP UP UP UP 172 NOD2 UP UP UP UP 173 DOWN DOWN DOWN DOWN 174 MCTP1 UP UP UP UP 175 BANK1 DOWN DOWN DOWN DOWN 176 ZNF30 DOWN DOWN DOWN DOWN 177 CTTN UP UP UP UP 178 PTCRA UP UP UP UP 179 FBXO7 DOWN DOWN UP DOWN 180 FBXO7 DOWN DOWN UP DOWN 181 ABLIM1 DOWN DOWN DOWN DOWN 182 LAMP3 DOWN UP UP UP 183 CEBPE UP UP UP UP 184 LOC646909 DOWN DOWN DOWN DOWN 185 BCL11B DOWN DOWN DOWN DOWN 186 TRIM58 DOWN DOWN UP UP 187 SAMD3 DOWN DOWN DOWN DOWN 188 SAMD3 DOWN DOWN DOWN DOWN 189 MYOF UP UP UP UP 190 TTPAL UP UP UP DOWN 191 LOC642934 DOWN DOWN DOWN DOWN 192 UP UP UP UP 193 SNORA28 UP UP UP UP 194 FLJ32255 UP DOWN UP UP 195 DOWN DOWN DOWN DOWN 196 LOC642073 DOWN DOWN UP UP 197 CAMKK2 UP UP UP UP 198 OAS2 UP UP UP UP 199 RASGRP1 DOWN DOWN DOWN DOWN 200 CAPG UP UP UP UP 201 LOC648343 DOWN DOWN DOWN DOWN 202 CETP UP UP UP UP 203 CETP UP UP UP UP 204 CXCR7 DOWN DOWN DOWN DOWN 205 UBASH3A DOWN DOWN DOWN DOWN 206 LOC284648 DOWN UP UP UP 207 IL1R2 UP UP UP UP 208 AGK DOWN DOWN DOWN DOWN 209 GTPBP8 DOWN DOWN DOWN DOWN 210 LEF1 DOWN DOWN DOWN DOWN 211 LEF1 DOWN DOWN DOWN DOWN 212 GPR109A UP UP UP UP 213 IFI35 UP UP UP UP 214 IRF7 UP UP UP UP 215 IRF7 UP UP UP UP 216 SP4 DOWN DOWN DOWN DOWN 217 IL2RB DOWN DOWN DOWN DOWN 218 ABLIM1 DOWN DOWN DOWN DOWN 219 TAPBP UP UP UP UP 220 MAL DOWN DOWN DOWN DOWN 221 TCEA3 DOWN DOWN DOWN DOWN 222 KREMEN1 UP UP UP UP 223 KREMEN1 UP UP UP UP 224 VNN1 UP UP UP UP 225 GBP1 DOWN UP UP UP 226 GBP1 DOWN UP UP UP 227 UBE2C UP UP UP UP 228 DET1 DOWN DOWN UP DOWN 229 ANKRD36 DOWN DOWN DOWN DOWN 230 DEFA4 UP UP UP UP 231 GCH1 UP UP UP UP 232 IL7R DOWN DOWN DOWN DOWN 233 TMCO3 UP UP DOWN UP 234 FBXO6 UP UP UP UP 235 LACTB UP UP UP UP 236 LOC730953 UP UP UP UP 237 LOC285296 UP UP UP UP 238 IL18R1 UP UP UP UP 239 UP UP UP UP 240 PRR5 DOWN DOWN UP DOWN 241 LOC400061 DOWN DOWN DOWN DOWN 242 TSEN2 DOWN DOWN DOWN DOWN 243 MGC15763 DOWN DOWN DOWN DOWN 244 SH3YL1 DOWN DOWN DOWN DOWN 245 ZNF337 DOWN DOWN DOWN DOWN 246 AFF3 DOWN DOWN DOWN DOWN 247 TYMS UP UP UP UP 248 ZCCHC14 DOWN DOWN DOWN DOWN 249 SLC6A12 UP UP UP UP 250 LY6E DOWN UP UP UP 251 KLF12 DOWN DOWN DOWN DOWN 252 LOC100132317 UP UP UP UP 253 TYW3 DOWN DOWN DOWN DOWN 254 BTLA DOWN DOWN DOWN DOWN 255 SLC24A4 UP UP UP UP 256 DOWN DOWN DOWN DOWN 257 NCALD DOWN DOWN DOWN DOWN 258 ORAI2 UP UP UP UP 259 ITGB3BP DOWN DOWN DOWN DOWN 260 GYPE UP UP UP UP 261 DOCKS UP UP UP UP 262 RASGRP4 UP UP UP UP 263 LOC339290 DOWN DOWN DOWN DOWN 264 PRF1 DOWN DOWN DOWN DOWN 265 TGFBR3 DOWN DOWN DOWN DOWN 266 LGALS9 UP UP UP UP 267 LGALS9 UP UP UP UP 268 BATF2 UP UP UP UP 269 MGC57346 DOWN DOWN DOWN DOWN 270 TXK DOWN DOWN DOWN DOWN 271 DHX58 UP DOWN UP UP 272 EPB41L3 UP UP UP UP 273 LOC100132499 UP DOWN DOWN DOWN 274 LOC100129674 UP UP UP UP 275 GDPD5 DOWN DOWN UP UP 276 ACP2 UP UP UP UP 277 C3AR1 UP UP UP UP 278 APOB48R UP UP UP UP 279 UTRN DOWN DOWN UP DOWN 280 SLC2A14 UP UP UP UP 281 CLEC4D UP UP UP UP 282 PKM2 UP UP UP UP 283 CDCA5 UP UP UP UP 284 CACNA1E UP UP UP UP 285 OSBPL3 DOWN DOWN DOWN DOWN 286 SLC22A15 UP UP UP UP 287 VPREB3 DOWN DOWN DOWN DOWN 288 LOC642780 UP UP UP UP 289 MEGF6 DOWN DOWN DOWN DOWN 290 LOC93622 DOWN DOWN DOWN DOWN 291 PFAS DOWN DOWN DOWN DOWN 292 LOC729389 DOWN DOWN DOWN DOWN 293 CREBZF UP DOWN DOWN DOWN 294 IMPDH1 UP UP UP UP 295 DHRS3 DOWN DOWN DOWN DOWN 296 AXIN2 DOWN DOWN DOWN DOWN 297 DDX60L UP UP UP UP 298 UP UP UP UP 299 RPGRIP1 UP DOWN UP DOWN 300 GPR160 UP UP UP UP 301 TMTC1 UP UP UP UP 302 ABCA2 UP UP DOWN UP 303 CEACAM1 UP UP UP UP 304 CEACAM1 UP UP UP UP 305 FLJ42957 UP UP UP UP 306 SIAH2 UP UP UP UP 307 DDAH2 UP UP UP UP 308 C13ORF18 UP UP DOWN DOWN 309 TAGLN UP UP UP UP 310 LCN2 UP UP UP UP 311 RELB UP UP UP UP 312 NR1I2 UP UP UP UP 313 BEND7 UP UP UP UP 314 PIK3C2B DOWN DOWN DOWN DOWN 315 IFI6 UP UP UP UP 316 DUT DOWN DOWN DOWN DOWN 317 SETD6 DOWN DOWN DOWN DOWN 318 DOWN DOWN DOWN DOWN 319 LOC100131572 DOWN DOWN DOWN DOWN 320 TNRC6A DOWN DOWN UP DOWN 321 LOC399744 UP UP UP UP 322 MAPK13 UP UP DOWN UP 323 TAP2 UP UP UP UP 324 CCDC15 DOWN DOWN UP DOWN 325 TNCRNA UP UP UP UP 326 SIPA1L2 UP UP UP UP 327 HIST1H4E DOWN UP UP UP 328 PTPRE UP UP UP UP 329 ELANE UP UP UP UP 330 TGM2 UP UP UP UP 331 ARSD UP UP UP UP 332 LOC651451 DOWN DOWN DOWN DOWN 333 CYFIP1 UP UP UP UP 334 CYFIP1 UP UP UP UP 335 UP UP UP UP 336 PPIA DOWN DOWN DOWN DOWN 337 LOC642255 UP UP DOWN UP 338 ASCC2 DOWN DOWN UP DOWN 339 ZNF827 DOWN DOWN DOWN DOWN 340 STAB1 UP UP UP UP 341 DNASE1L1 UP UP UP UP 342 LMNB1 UP UP UP UP 343 MAP4K1 DOWN DOWN DOWN DOWN 344 PSMB9 UP UP UP UP 345 ATF3 UP UP UP UP 346 CPEB4 UP UP UP UP 347 ATP5S DOWN DOWN UP DOWN 348 CD5 DOWN DOWN DOWN DOWN 349 SYTL2 DOWN DOWN DOWN DOWN 350 H2AFJ UP UP UP UP 351 HP UP UP UP UP 352 SORT1 UP UP UP UP 353 KLHL18 UP UP UP UP 354 HIST1H2BK UP UP UP UP 355 HEMGN DOWN DOWN UP DOWN 356 KRTAP19-6 UP UP UP UP 357 RNASE2 UP UP UP UP 358 RAB13 UP UP UP UP 359 LOC100134393 DOWN DOWN DOWN DOWN 360 C11ORF82 UP UP UP UP 361 BLK DOWN DOWN DOWN DOWN 362 CD160 DOWN DOWN DOWN DOWN 363 NFIA DOWN DOWN UP UP 364 LOC100128460 UP UP UP UP 365 CD19 DOWN DOWN DOWN DOWN 366 ZNF438 UP UP UP UP 367 MBNL3 DOWN DOWN UP DOWN 368 MBNL3 DOWN DOWN UP DOWN 369 UP UP UP UP 370 LOC729010 UP UP UP UP 371 NAGA UP UP UP UP 372 FCER1A DOWN DOWN DOWN DOWN 373 C6ORF25 UP UP UP UP 374 SLC22A4 UP UP UP UP 375 LOC729686 DOWN DOWN DOWN DOWN 376 LOC728843 DOWN DOWN DOWN DOWN 377 CTSL1 DOWN UP UP UP 378 BCL11A DOWN DOWN DOWN DOWN 379 ACTA2 UP UP UP UP 380 KIAA1632 UP UP UP UP 381 UBE2C UP UP UP UP 382 CASP4 UP UP UP UP 383 SLC22A4 UP UP UP UP 384 SFT2D2 UP UP UP UP 385 TLR2 UP UP UP UP 386 C10ORF105 UP UP UP UP 387 EIF2AK2 UP UP UP UP 388 TATDN1 DOWN DOWN DOWN DOWN 389 RAB24 UP UP UP UP 390 FAH UP UP UP UP 391 DOWN DOWN DOWN DOWN 392 DISC1 UP UP UP UP 393 LOC641848 DOWN DOWN DOWN DOWN 394 ARG1 UP UP UP UP 395 LCK DOWN DOWN DOWN DOWN 396 WDFY3 UP UP UP UP 397 RNF165 DOWN DOWN DOWN DOWN 398 MLKL UP UP UP UP 399 LOC100132673 DOWN DOWN DOWN DOWN 400 ANKDD1A UP UP UP UP 401 MSRB3 UP UP UP UP 402 LOC100134379 UP UP UP UP 403 MEFV UP UP UP UP 404 C12ORF57 DOWN DOWN DOWN DOWN 405 CCDC102A DOWN DOWN DOWN DOWN 406 LOC731777 DOWN DOWN UP DOWN 407 LOC729040 UP UP UP UP 408 TBC1D8 UP UP UP UP 409 KLRF1 DOWN DOWN DOWN DOWN 410 KLRF1 DOWN DOWN DOWN DOWN 411 ABCA1 UP UP UP UP 412 LOC650761 DOWN DOWN DOWN DOWN 413 LOC653867 UP UP DOWN UP 414 LOC648710 UP UP UP UP 415 SLC2A11 UP UP UP UP 416 LOC652578 UP UP UP UP 417 GPR114 DOWN DOWN UP DOWN 418 MANSC1 UP UP DOWN UP 419 MANSC1 UP UP DOWN UP 420 DGKA DOWN DOWN DOWN DOWN 421 LIN7A UP UP UP UP 422 ITPRIPL2 UP UP UP UP 423 ANO9 DOWN DOWN DOWN DOWN 424 KCNJ15 UP UP UP UP 425 KCNJ15 UP UP UP UP 426 LOC389386 UP UP UP UP 427 LOC100132960 UP UP UP UP 428 LOC643332 UP UP UP UP 429 SFI1 DOWN DOWN DOWN DOWN 430 ABCE1 DOWN DOWN DOWN DOWN 431 ABCE1 DOWN DOWN DOWN DOWN 432 SERPINA1 UP UP UP UP 433 OR2W3 DOWN DOWN UP DOWN 434 ABI3 DOWN DOWN UP DOWN 435 LOC400759 UP UP UP UP 436 UP UP DOWN UP 437 LOC728519 UP UP UP UP 438 LOC654053 UP UP UP UP 439 LOC649553 DOWN DOWN DOWN DOWN 440 UP UP UP UP 441 HSD17B8 DOWN DOWN DOWN DOWN 442 C16ORF30 DOWN DOWN DOWN DOWN 443 GADD45G UP UP UP UP 444 TPST1 UP UP UP UP 445 GNG7 DOWN DOWN DOWN DOWN 446 SV2A UP UP UP UP 447 LOC649946 DOWN DOWN DOWN DOWN 448 LOC100129697 UP UP UP UP 449 DOWN DOWN DOWN DOWN 450 RARRES3 DOWN DOWN UP UP 451 C8ORF83 UP UP UP UP 452 TNFSF13B UP UP UP UP 453 DOWN DOWN DOWN DOWN 454 SNRPD3 UP DOWN DOWN DOWN 455 LOC645232 UP UP UP UP 456 PI3 UP UP UP DOWN 457 WDFY1 UP UP UP UP 458 LOC100134660 UP UP UP UP 459 LOC100133678 DOWN DOWN UP UP 460 BAMBI UP UP UP UP 461 POP5 DOWN DOWN DOWN DOWN 462 TARBP1 DOWN DOWN DOWN DOWN 463 IRAK3 UP UP UP UP 464 ZNF7 DOWN DOWN DOWN DOWN 465 NLRC4 UP UP UP UP 466 SKAP1 DOWN DOWN DOWN DOWN 467 GAS7 UP UP UP UP 468 C12ORF29 DOWN DOWN DOWN DOWN 469 KLRD1 DOWN DOWN DOWN DOWN 470 DOWN DOWN DOWN DOWN 471 ABHD15 DOWN DOWN DOWN DOWN 472 CCDC146 UP DOWN UP UP 473 CASP5 UP UP UP UP 474 AARS2 DOWN DOWN DOWN DOWN 475 LOC642103 UP UP UP UP 476 LOC730385 UP UP UP UP 477 GAR1 DOWN DOWN DOWN DOWN 478 MAF DOWN DOWN DOWN DOWN 479 ARAP2 UP UP UP UP 480 C16ORF7 UP UP UP UP 481 HLA-C UP DOWN DOWN UP 482 FLJ22662 UP UP UP UP 483 DACH1 UP UP UP UP 484 CRY1 DOWN DOWN DOWN DOWN 485 CRY1 DOWN DOWN DOWN DOWN 486 LRRC25 UP UP UP UP 487 KIAA0564 DOWN DOWN DOWN DOWN 488 UPF3A DOWN DOWN DOWN DOWN 489 MARCO UP UP UP UP 490 LOC100132564 UP UP DOWN UP 491 SRPRB DOWN DOWN DOWN DOWN 492 MAD1L1 DOWN DOWN DOWN DOWN 493 LOC653610 UP UP UP UP 494 P4HTM DOWN DOWN DOWN DOWN 495 CCL4L1 DOWN DOWN DOWN DOWN 496 LAPTM4B UP UP DOWN UP 497 MAPK14 UP UP UP UP 498 CD96 DOWN DOWN DOWN DOWN 499 TLR7 UP UP UP UP 500 KCNMB1 UP UP UP UP 501 HIP1 UP UP UP UP 502 P2RX7 UP UP UP UP 503 LOC650140 UP UP UP UP 504 LOC791120 DOWN DOWN DOWN DOWN 505 LTF UP UP UP UP 506 C3ORF75 DOWN DOWN DOWN DOWN 507 GPX7 DOWN DOWN DOWN DOWN 508 SPRYD5 DOWN DOWN UP DOWN 509 MOV10 DOWN UP UP UP 510 EEF1B2 DOWN DOWN DOWN DOWN 511 CTDSPL UP UP UP UP 512 HIST2H2BE UP UP UP UP 513 SLC38A1 DOWN DOWN DOWN DOWN 514 AIM2 UP UP UP UP 515 LOC100130904 UP UP DOWN UP 516 LOC650546 UP UP UP UP 517 P2RY10 DOWN DOWN DOWN DOWN 518 IL5RA DOWN DOWN UP DOWN 519 MMP8 UP UP UP UP 520 LOC100128485 UP UP UP UP 521 RPS23 DOWN DOWN DOWN DOWN 522 HDAC7 UP UP UP UP 523 GUCY1A3 UP UP UP UP 524 TGFA UP UP UP UP 525 NAIP UP UP UP UP 526 NAIP UP UP UP UP 527 NELL2 DOWN DOWN DOWN DOWN 528 SIDT1 DOWN DOWN DOWN DOWN 529 SLAMF1 DOWN DOWN DOWN DOWN 530 MAPK14 UP UP UP UP 531 CCR3 DOWN DOWN UP DOWN 532 MKNK1 UP UP UP UP 533 D4S234E DOWN DOWN DOWN DOWN 534 DOWN DOWN DOWN DOWN 535 NBN UP UP UP UP 536 LOC654346 DOWN UP UP UP 537 FGFBP2 DOWN DOWN DOWN DOWN 538 BTLA DOWN DOWN DOWN DOWN 539 PRMT1 DOWN DOWN DOWN DOWN 540 PDGFC UP UP UP UP 541 LRRN3 DOWN DOWN DOWN DOWN 542 MT2A DOWN DOWN UP UP 543 LOC728790 UP UP UP UP 544 LOC646672 DOWN DOWN DOWN DOWN 545 NTN3 UP UP UP UP 546 CD8A DOWN DOWN DOWN DOWN 547 CD8A DOWN DOWN DOWN DOWN 548 ZBP1 UP UP UP UP 549 LDOC1L DOWN DOWN DOWN DOWN 550 CHM DOWN DOWN DOWN DOWN 551 LOC440731 UP UP UP UP 552 LOC100131787 DOWN DOWN DOWN DOWN 553 TNFRSF10C UP UP UP UP 554 LOC651612 UP UP DOWN UP 555 STX11 UP UP UP UP 556 LOC100128060 DOWN DOWN DOWN DOWN 557 C1QB UP UP UP UP 558 PVRL2 UP UP UP UP 559 ZMYND15 UP UP UP UP 560 TRAPPC2P1 DOWN DOWN DOWN DOWN 561 SECTM1 UP UP UP UP 562 TRAT1 DOWN DOWN DOWN DOWN 563 CAMKK2 UP UP UP UP 564 CXCR5 DOWN DOWN DOWN DOWN 565 CD163 UP UP UP UP 566 FAS UP UP UP UP 567 RPL12P6 DOWN DOWN DOWN DOWN 568 LOC100134734 UP UP UP UP 569 CD36 UP UP UP UP 570 FCGR1B UP UP UP UP 571 NR3C2 DOWN DOWN DOWN DOWN 572 CSGALNACT2 UP UP UP UP 573 NCRNA00085 UP UP UP UP 574 GATA2 DOWN DOWN UP DOWN 575 EBI2 DOWN DOWN DOWN DOWN 576 EBI2 DOWN DOWN DOWN DOWN 577 FKBP5 UP UP UP UP 578 CRISPLD2 UP UP UP UP 579 LOC152195 UP UP UP UP 580 LOC100132199 DOWN DOWN DOWN DOWN 581 DGAT2 UP UP UP UP 582 SCML1 DOWN DOWN DOWN DOWN 583 LSS DOWN DOWN DOWN DOWN 584 CIITA DOWN DOWN UP UP 585 SAP30 UP UP UP UP 586 TLR5 UP UP UP UP 587 NFATC3 DOWN DOWN DOWN DOWN 588 NAMPT UP UP UP UP 589 GZMK DOWN DOWN DOWN DOWN 590 CARD17 UP UP UP UP 591 INCA UP UP UP UP 592 MSL3L1 UP UP UP UP 593 CD8A DOWN DOWN DOWN DOWN 594 MIIP UP UP UP UP 595 SRPK1 UP UP UP UP 596 SLC6A6 UP UP UP UP 597 C10ORF119 UP UP UP UP 598 C17ORF60 UP UP UP UP 599 LOC642816 UP UP UP UP 600 AKR1C3 DOWN DOWN DOWN DOWN 601 LHFPL2 UP UP UP UP 602 CR1 UP UP UP UP 603 KIAA1026 UP UP UP UP 604 CCDC91 DOWN DOWN DOWN DOWN 605 FAM102A DOWN DOWN DOWN DOWN 606 FAM102A DOWN DOWN DOWN DOWN 607 UPRT DOWN DOWN DOWN DOWN 608 UP UP DOWN UP 609 PLEKHA1 DOWN DOWN DOWN DOWN 610 GIMAP7 DOWN DOWN DOWN DOWN 611 CACNA2D3 DOWN DOWN DOWN DOWN 612 DDX10 DOWN DOWN DOWN DOWN 613 RPL23A DOWN DOWN DOWN DOWN 614 C2ORF44 DOWN DOWN DOWN DOWN 615 LSP1 UP UP UP UP 616 C7ORF53 UP UP UP UP 617 LOC100130905 DOWN DOWN UP DOWN 618 DNAJC5 UP UP UP UP 619 SLAIN1 DOWN DOWN DOWN DOWN 620 CDKN1C DOWN DOWN UP UP 621 AKAP7 DOWN DOWN DOWN DOWN 622 HIATL1 UP UP UP UP 623 CRELD1 DOWN DOWN DOWN DOWN 624 ZNHIT6 DOWN DOWN DOWN DOWN 625 TIFA DOWN UP UP UP 626 ARL4C DOWN DOWN DOWN DOWN 627 PIGU DOWN DOWN DOWN DOWN 628 MEF2A UP UP UP UP 629 PIK3CB UP UP UP UP 630 CDK5RAP2 UP UP UP UP 631 FLNB DOWN DOWN DOWN DOWN 632 GRAP DOWN DOWN DOWN DOWN 633 TLE3 UP UP UP UP 634 BATF UP UP UP UP 635 CYP4F3 UP UP UP UP 636 DOWN DOWN DOWN DOWN 637 KIR2DL3 DOWN DOWN DOWN DOWN 638 C19ORF59 UP UP UP UP 639 NRG1 UP UP UP UP 640 PPP2R2B DOWN DOWN DOWN DOWN 641 CDK5RAP2 UP UP UP UP 642 PLSCR1 UP UP UP UP 643 UBL7 DOWN DOWN UP DOWN 644 HES4 DOWN DOWN UP UP 645 ZNF256 DOWN DOWN DOWN DOWN 646 DKFZP761E198 UP UP UP UP 647 SAMD14 UP UP UP UP 648 BAG3 DOWN DOWN DOWN DOWN 649 PARP14 UP UP UP UP 650 MS4A7 UP DOWN UP UP 651 ECHDC3 UP UP UP UP 652 OCIAD2 DOWN DOWN DOWN DOWN 653 LOC90925 DOWN DOWN DOWN DOWN 654 RGL4 UP UP DOWN UP 655 PARP9 UP UP UP UP 656 PARP9 UP UP UP UP 657 CD151 UP UP UP UP 658 SAAL1 DOWN DOWN DOWN DOWN 659 LOC388076 DOWN DOWN DOWN DOWN 660 SIGLEC5 UP UP UP UP 661 LRIG1 DOWN DOWN DOWN DOWN 662 PTGDR DOWN DOWN DOWN DOWN 663 PTGDR DOWN DOWN DOWN DOWN 664 NBPF8 UP UP DOWN DOWN 665 NHS UP DOWN DOWN DOWN 666 ACSL1 UP UP UP UP 667 HK3 UP UP UP UP 668 SNX20 UP UP UP UP 669 F2RL1 UP UP UP UP 670 F2RL1 UP UP UP UP 671 PARP12 DOWN DOWN UP UP 672 LOC441506 DOWN DOWN DOWN DOWN 673 MFGE8 DOWN DOWN DOWN DOWN 674 SERPINA10 DOWN DOWN DOWN DOWN 675 FAM69A DOWN DOWN DOWN DOWN 676 IL4R UP UP DOWN UP 677 KIAA1671 DOWN DOWN DOWN DOWN 678 OAS3 DOWN UP UP UP 679 PRR5 DOWN DOWN UP DOWN 680 TMEM194 DOWN DOWN DOWN DOWN 681 MS4A1 DOWN DOWN DOWN DOWN 682 NRSN2 UP UP UP UP 683 MTHFD2 UP UP UP UP 684 LOC400793 UP UP DOWN UP 685 CEACAM1 UP UP UP UP 686 RPL37 DOWN DOWN DOWN DOWN 687 APP UP UP DOWN DOWN 688 RRBP1 UP UP UP UP 689 SLCO4C1 UP UP DOWN DOWN 690 XAF1 DOWN DOWN UP UP 691 XAF1 DOWN UP UP UP 692 SLC2A6 DOWN UP UP UP 693 ZNF831 DOWN DOWN DOWN DOWN 694 ZNF831 DOWN DOWN DOWN DOWN 695 POLR1C DOWN DOWN DOWN DOWN 696 GLT1D1 UP UP UP UP 697 VDR UP UP UP UP 698 IFIT5 UP UP UP UP 699 CSTA UP UP UP UP 700 SNHG8 DOWN DOWN DOWN DOWN 701 TOP1MT DOWN DOWN DOWN DOWN 702 UPP1 UP UP UP UP 703 SYTL2 DOWN DOWN DOWN DOWN 704 LOC440359 DOWN DOWN UP UP 705 KLRB1 DOWN DOWN DOWN DOWN 706 MTMR3 UP UP UP UP 707 S1PR1 DOWN DOWN DOWN DOWN 708 FYB UP UP UP UP 709 CDC20 UP UP UP UP 710 MEX3C DOWN DOWN DOWN DOWN 711 FAM168B DOWN DOWN DOWN DOWN 712 C20ORF107 UP UP UP UP 713 SLC4A7 DOWN DOWN DOWN DOWN 714 CD79B DOWN DOWN DOWN DOWN 715 FAM84B DOWN DOWN DOWN DOWN 716 LOC100134688 UP UP UP UP 717 LOC651738 UP UP UP UP 718 PLAGL1 UP UP UP UP 719 TIMM10 DOWN UP UP UP 720 LOC641710 UP UP UP UP 721 TRAF5 DOWN DOWN DOWN DOWN 722 TAP1 UP UP UP UP 723 FCRL2 DOWN DOWN DOWN DOWN 724 SRC UP UP UP UP 725 RALGAPA1 DOWN DOWN DOWN DOWN 726 OCIAD2 DOWN DOWN DOWN DOWN 727 PON2 DOWN DOWN DOWN DOWN 728 LOC730029 DOWN DOWN DOWN DOWN 729 LOC100134768 UP UP UP UP 730 LOC100134241 DOWN DOWN DOWN DOWN 731 LOC26010 DOWN DOWN UP UP 732 PLA2G12A UP UP DOWN UP 733 BACH1 UP UP UP UP 734 DSC1 DOWN DOWN DOWN DOWN 735 NOB1 UP DOWN DOWN DOWN 736 LOC645693 DOWN DOWN DOWN DOWN 737 LOC643313 UP UP DOWN UP 738 BTBD11 DOWN DOWN DOWN DOWN 739 TMEM169 UP UP UP UP 740 REPS2 UP UP UP UP 741 ZNF23 DOWN DOWN DOWN DOWN 742 C18ORF55 DOWN DOWN DOWN DOWN 743 APOL2 UP UP UP UP 744 APOL2 UP UP UP UP 745 PASK DOWN DOWN DOWN DOWN 746 FER1L3 UP UP UP UP 747 U2AF1 UP UP DOWN DOWN 748 LOC285359 DOWN DOWN DOWN DOWN 749 SIGLEC14 UP UP UP DOWN 750 ARL1 DOWN DOWN DOWN DOWN 751 C19ORF62 DOWN DOWN UP DOWN 752 NCR3 DOWN DOWN DOWN DOWN 753 UP UP UP UP 754 HOXB2 DOWN DOWN DOWN DOWN 755 RNF135 UP UP UP UP 756 IFIT1 UP UP UP UP 757 GCAT UP DOWN UP UP 758 KLF12 DOWN DOWN DOWN DOWN 759 LILRB2 DOWN UP UP UP 760 LOC728835 DOWN DOWN DOWN DOWN 761 GSN UP UP UP UP 762 LOC100008589 UP DOWN DOWN UP 763 LOC100008589 UP UP DOWN UP 764 FLJ14213 DOWN DOWN UP UP 765 SH2D3C UP UP UP UP 766 LOC100133177 UP UP UP UP 767 TMEM176A UP UP UP UP 768 HIST2H2AB UP UP UP UP 769 KIAA1618 UP UP UP UP 770 CMTM5 UP UP UP UP 771 C21ORF2 DOWN DOWN DOWN DOWN 772 CREB5 UP UP UP UP 773 FAS UP UP UP UP 774 MTF1 UP UP UP UP 775 RSAD2 UP UP UP UP 776 ANPEP UP UP UP UP 777 C14ORF179 DOWN DOWN DOWN DOWN 778 TXNL4B UP UP UP UP 779 MYL9 UP UP UP UP 780 MYL9 UP UP UP UP 781 LOC100130828 UP UP UP UP 782 LOC391019 DOWN DOWN DOWN DOWN 783 ITGA2B UP UP UP UP 784 KLRC3 DOWN DOWN DOWN DOWN 785 RASGRP2 DOWN DOWN DOWN DOWN 786 NDST1 UP UP UP UP 787 LOC388344 DOWN DOWN DOWN DOWN 788 IFI6 DOWN UP UP UP 789 OAS1 UP UP UP UP 790 OAS1 UP UP UP UP 791 TRIM10 DOWN DOWN UP DOWN 792 LIMK2 UP UP UP UP 793 LIMK2 UP UP UP UP 794 ATP5S DOWN DOWN DOWN DOWN 795 SMARCD3 UP UP UP UP 796 PHC2 UP UP UP UP 797 SOX8 DOWN DOWN DOWN DOWN 798 LCK DOWN DOWN DOWN DOWN 799 DOWN DOWN DOWN DOWN 800 SAMD9L UP UP UP UP 801 EHBP1 DOWN DOWN DOWN DOWN 802 E2F2 DOWN DOWN UP DOWN 803 CEACAM6 UP UP UP UP 804 LOC100132394 UP DOWN DOWN UP 805 LOC728014 DOWN DOWN DOWN DOWN 806 LOC728014 DOWN DOWN DOWN DOWN 807 SIRPG DOWN DOWN DOWN DOWN 808 OPLAH UP UP UP UP 809 FTHL2 UP UP UP UP 810 CXORF21 UP UP UP UP 811 CACNG6 DOWN DOWN UP DOWN 812 C11ORF75 UP UP UP UP 813 LY9 DOWN DOWN DOWN DOWN 814 LILRB4 UP UP UP UP 815 STAT2 UP UP UP UP 816 RAB20 UP UP UP UP 817 SOCS1 DOWN UP UP UP 818 PLOD2 UP UP UP UP 819 UGDH DOWN DOWN DOWN DOWN 820 MAK16 DOWN DOWN DOWN DOWN 821 ITGB3 UP UP UP UP 822 DHRS9 UP UP UP UP 823 PLEKHF1 DOWN DOWN DOWN DOWN 824 ASAP1IT1 UP UP UP UP 825 PSME2 DOWN UP UP UP 826 UP UP UP UP 827 LOC100128269 UP UP DOWN UP 828 ALX1 UP UP UP UP 829 BAK1 DOWN UP UP UP 830 XPO4 DOWN DOWN DOWN DOWN 831 CD247 DOWN DOWN DOWN DOWN 832 C3ORF26 DOWN DOWN DOWN DOWN 833 FAM43A DOWN DOWN DOWN DOWN 834 ICOS DOWN DOWN DOWN DOWN 835 ISG15 UP UP UP UP 836 UP UP UP UP 837 HIST2H2AA4 UP UP UP UP 838 CD79A DOWN DOWN DOWN DOWN 839 SLC25A4 DOWN DOWN DOWN DOWN 840 TMEM158 UP UP UP UP 841 FANCD2 DOWN DOWN DOWN DOWN 842 GPR18 DOWN DOWN DOWN DOWN 843 LAP3 UP UP UP UP 844 TNFSF13B UP UP UP UP 845 TC2N DOWN DOWN DOWN DOWN 846 HSF2 DOWN DOWN DOWN DOWN 847 CD7 DOWN DOWN DOWN DOWN 848 C20ORF3 UP UP UP UP 849 HLA-DRB3 DOWN DOWN UP UP 850 SESN1 DOWN DOWN DOWN DOWN 851 LOC347376 UP UP UP UP 852 P2RY14 DOWN UP UP UP 853 P2RY14 UP UP UP UP 854 P2RY14 DOWN UP UP UP 855 CYP1B1 UP UP DOWN UP 856 IFIT3 DOWN UP UP UP 857 IFIT3 UP UP UP UP 858 RPL13L DOWN DOWN DOWN DOWN 859 LOC729423 DOWN DOWN DOWN DOWN 860 DBN1 UP UP UP UP 861 TTC27 DOWN DOWN DOWN DOWN 862 DPH5 DOWN DOWN DOWN DOWN 863 GPR141 UP UP UP UP 864 RBBP8 UP UP UP UP 865 LOC654350 DOWN DOWN DOWN DOWN 866 SLC30A1 UP UP UP UP 867 PRSS23 DOWN DOWN DOWN DOWN 868 JAM3 UP UP UP UP 869 GNPDA2 DOWN DOWN DOWN DOWN 870 IL7R DOWN DOWN DOWN DOWN 871 ACAD11 DOWN DOWN DOWN DOWN 872 LOC642788 UP UP UP UP 873 ALPK1 UP UP UP UP 874 LOC439949 DOWN DOWN DOWN DOWN 875 UP UP UP UP 876 BCAT1 UP UP UP UP 877 C9ORF114 DOWN DOWN DOWN DOWN 878 ATPGD1 DOWN DOWN DOWN DOWN 879 TREML1 UP UP UP UP 880 PECR UP UP DOWN DOWN 881 SPATA13 UP DOWN DOWN UP 882 MAN1C1 DOWN DOWN DOWN DOWN 883 IDO1 DOWN DOWN UP UP 884 TSEN54 DOWN DOWN DOWN DOWN 885 SCRN1 DOWN DOWN UP DOWN 886 LOC441193 UP UP UP UP 887 LOC202134 DOWN DOWN DOWN DOWN 888 KIAA0319L UP UP UP UP 889 TIAM2 UP UP DOWN DOWN 890 MOSC1 UP UP UP UP 891 PFKFB3 UP UP UP UP 892 GNB4 UP UP UP UP 893 ANKRD22 UP UP UP UP 894 PROS1 UP UP UP UP 895 CD40LG DOWN DOWN DOWN DOWN 896 RIOK2 DOWN DOWN DOWN DOWN 897 AFF1 UP UP UP UP 898 HIST1H3D UP UP UP UP 899 SLC26A8 UP UP UP UP 900 SLC26A8 UP UP UP UP 901 RNASE3 UP UP UP UP 902 UBE2L6 DOWN UP UP UP 903 UBE2L6 DOWN UP UP UP 904 SSH1 UP UP DOWN UP 905 KRBA1 DOWN DOWN DOWN DOWN 906 SLC25A23 DOWN DOWN DOWN DOWN 907 DTX3L UP UP UP UP 908 DOK3 UP UP UP UP 909 LOC644615 UP UP UP UP 910 SULT1B1 UP UP DOWN UP 911 RASGRP4 UP UP UP UP 912 ALOX15B UP UP UP UP 913 ADM UP UP UP UP 914 LOC391825 DOWN DOWN DOWN DOWN 915 LOC730234 UP UP UP UP 916 HIST2H2AA3 UP UP UP UP 917 HIST2H2AA3 UP UP UP UP 918 LIMK2 UP UP UP UP 919 MMRN1 UP UP UP UP 920 PADI2 UP UP DOWN UP 921 FKBP1A UP UP UP UP 922 GYG1 UP UP UP UP 923 UP UP DOWN UP 924 ASF1A DOWN DOWN DOWN DOWN 925 CD248 DOWN DOWN DOWN DOWN 926 CD3G DOWN DOWN DOWN DOWN 927 DEFA1 UP UP UP UP 928 EPHX2 DOWN DOWN DOWN DOWN 929 CST7 UP UP DOWN UP 930 ABLIM3 UP UP UP UP 931 ANKRD55 DOWN UP DOWN DOWN 932 SLC45A3 DOWN DOWN UP DOWN 933 RAB33B UP UP UP UP 934 LILRA6 UP UP UP UP 935 LILRA6 UP UP UP UP 936 SPTLC2 UP UP UP UP 937 CDA UP UP UP UP 938 PGD UP UP UP UP 939 LOC100130769 DOWN DOWN UP UP 940 ECHDC2 DOWN DOWN DOWN DOWN 941 KIF20B DOWN DOWN DOWN DOWN 942 B3GNT8 UP UP UP UP 943 PYHIN1 DOWN DOWN DOWN DOWN 944 LBH DOWN DOWN DOWN DOWN 945 LBH DOWN DOWN DOWN DOWN 946 UP UP UP UP 947 BPI UP UP UP UP 948 GAR1 DOWN DOWN DOWN DOWN 949 ST3GAL4 UP UP DOWN UP 950 TMEM19 DOWN DOWN DOWN DOWN 951 DHRS12 UP UP UP UP 952 DHRS12 UP UP UP UP 953 UP UP UP UP 954 FAM26F DOWN UP UP UP 955 FCRLA DOWN DOWN DOWN DOWN 956 OSBPL7 DOWN DOWN DOWN DOWN 957 CTSB UP DOWN UP UP 958 ALDH1A1 UP DOWN UP UP 959 SRRD DOWN DOWN UP DOWN 960 TOLLIP UP UP UP UP 961 ICAM1 UP UP UP UP 962 LAX1 DOWN DOWN DOWN DOWN 963 CASP7 UP UP UP UP 964 ZDHHC19 UP UP UP UP 965 LOC732371 UP UP UP UP 966 DENND1A UP UP UP UP 967 EMR2 UP UP UP UP 968 LOC643308 DOWN DOWN DOWN DOWN 969 ADA DOWN DOWN UP DOWN 970 LOC646527 DOWN DOWN DOWN DOWN 971 LOC643313 UP UP UP UP 972 GZMB DOWN DOWN DOWN DOWN 973 OLIG2 DOWN UP UP DOWN 974 GRINA DOWN UP UP UP 975 HLA-DPB1 DOWN DOWN UP UP 976 MX1 DOWN UP UP UP 977 THOC3 DOWN DOWN DOWN DOWN 978 CHST13 UP UP UP DOWN 979 TRPM6 UP UP UP UP 980 GK UP UP UP UP 981 JAK2 UP UP UP UP 982 ARHGEF11 UP UP UP UP 983 ARHGEF11 UP UP UP UP 984 HOMER2 UP UP UP UP 985 TACSTD2 UP UP UP UP 986 CA4 UP UP UP UP 987 GAA UP UP UP UP 988 IFITM3 UP UP UP UP 989 CLYBL DOWN DOWN DOWN DOWN 990 CLYBL DOWN DOWN DOWN DOWN 991 ANGPT1 UP DOWN UP DOWN 992 MME UP UP UP UP 993 ZNF408 UP UP UP UP 994 STAT1 UP UP UP UP 995 STAT1 UP UP UP UP 996 PNPLA7 DOWN DOWN DOWN DOWN 997 INDO DOWN UP UP UP 998 PDZD8 UP UP UP UP 999 PDGFD DOWN DOWN DOWN DOWN 1000 CTSL1 UP UP UP UP 1001 HOMER3 UP UP UP UP 1002 CEP78 DOWN DOWN DOWN DOWN 1003 SBK1 DOWN DOWN DOWN DOWN 1004 ALG9 DOWN DOWN DOWN DOWN 1005 KIF27 UP DOWN UP UP 1006 IL1R2 UP UP UP UP 1007 RAB40B DOWN DOWN DOWN DOWN 1008 MMP23B DOWN DOWN DOWN DOWN 1009 UP UP UP UP 1010 PGLYRP1 UP UP UP UP 1011 UHRF1 UP UP UP UP 1012 IFI44L DOWN UP UP UP 1013 PARP10 DOWN UP UP UP 1014 PARP10 UP UP UP UP 1015 GOLGA8A DOWN DOWN DOWN DOWN 1016 CCR7 DOWN DOWN DOWN DOWN 1017 HEMGN DOWN DOWN DOWN DOWN 1018 TCF7 DOWN DOWN DOWN DOWN 1019 CLUAP1 DOWN DOWN DOWN DOWN 1020 LOC390735 DOWN DOWN DOWN DOWN 1021 LOC641849 DOWN DOWN DOWN DOWN 1022 TYMP UP UP UP UP 1023 DEFA1B UP UP UP UP 1024 DEFA1B UP UP UP UP 1025 DEFA1B UP UP UP UP 1026 REPS2 UP UP UP UP 1027 REPS2 UP UP UP UP 1028 ZNF550 DOWN DOWN DOWN DOWN 1029 OSBPL1A UP UP DOWN DOWN 1030 C11ORF1 DOWN DOWN DOWN DOWN 1031 MCTP2 UP UP UP UP 1032 EMR4 DOWN DOWN UP UP 1033 LOC653316 DOWN DOWN DOWN DOWN 1034 UP UP UP UP 1035 FCRL6 DOWN DOWN DOWN DOWN 1036 MRPS26 DOWN DOWN DOWN DOWN 1037 RHOBTB3 DOWN DOWN UP UP 1038 DIRC2 UP UP UP UP 1039 CD27 DOWN DOWN DOWN DOWN 1040 PLEKHG4 DOWN DOWN DOWN DOWN 1041 CDH6 UP UP UP UP 1042 C4ORF23 UP UP UP UP 1043 HIST2H2AC UP UP UP UP 1044 SLC7A6 DOWN DOWN DOWN DOWN 1045 SLC7A6 DOWN DOWN DOWN DOWN 1046 SLAMF6 DOWN DOWN DOWN DOWN 1047 RETN UP UP DOWN UP 1048 FAIM3 DOWN DOWN DOWN DOWN 1049 PIK3C2A DOWN DOWN DOWN DOWN 1050 TMEM99 DOWN DOWN DOWN DOWN 1051 LOC728411 DOWN DOWN DOWN DOWN 1052 TMEM194A DOWN DOWN DOWN DOWN 1053 NAPEPLD DOWN DOWN DOWN DOWN 1054 ACOX1 UP UP UP UP 1055 CTLA4 DOWN DOWN DOWN DOWN 1056 SCO2 UP UP UP UP 1057 STK3 UP UP UP UP 1058 FLT3LG DOWN DOWN DOWN DOWN 1059 VASP UP UP UP UP 1060 FBXO31 DOWN DOWN DOWN DOWN 1061 TDRD9 UP UP DOWN UP 1062 TDRD9 UP UP UP UP 1063 LOC646144 UP UP UP UP 1064 NUSAP1 UP UP UP UP 1065 GPR97 UP UP UP UP 1066 GPR97 UP UP UP UP 1067 GPR97 UP UP UP UP 1068 EMR1 DOWN UP UP UP 1069 NR1H3 DOWN UP UP UP 1070 SLAMF6 DOWN DOWN DOWN DOWN 1071 CCDC106 DOWN DOWN DOWN DOWN 1072 ODF3B UP UP UP UP 1073 LOC100129904 UP UP UP UP 1074 PADI4 UP UP UP UP 1075 LOC100132858 UP UP UP UP 1076 PIK3AP1 UP UP UP UP 1077 ZNF792 DOWN DOWN DOWN DOWN 1078 DIP2A DOWN DOWN DOWN DOWN 1079 OSCAR UP UP UP UP 1080 DOWN DOWN DOWN DOWN 1081 CLIC3 DOWN DOWN DOWN DOWN 1082 FANCE DOWN DOWN DOWN DOWN 1083 TECPR2 UP UP UP UP 1084 P2RY10 DOWN DOWN DOWN DOWN 1085 ADORA3 UP UP UP UP 1086 IL18RAP UP UP DOWN UP 1087 DEFA3 UP UP UP UP 1088 BRSK1 UP UP UP UP 1089 LOC647691 UP UP UP UP 1090 ALG8 DOWN DOWN DOWN DOWN 1091 S1PR5 DOWN DOWN DOWN DOWN 1092 CPA3 DOWN DOWN UP DOWN 1093 BMX UP UP UP UP 1094 DDX58 UP UP UP UP 1095 RHOBTB1 UP UP UP UP 1096 TNFRSF25 DOWN DOWN DOWN DOWN 1097 LOC730387 UP UP UP UP 1098 OLR1 UP UP UP UP 1099 HERC5 UP UP UP UP 1100 STAT1 UP UP UP UP 1101 NELF DOWN DOWN DOWN DOWN 1102 STAP1 DOWN DOWN DOWN DOWN 1103 SLC2A5 UP UP UP UP 1104 ITGB5 UP UP UP UP 1105 ZNF516 UP UP UP UP 1106 ARHGAP26 UP UP UP UP 1107 TIMP2 UP UP UP UP 1108 FCGR1A UP UP UP UP 1109 RHOH DOWN DOWN DOWN DOWN 1110 IFI44 UP UP UP UP 1111 MTX3 DOWN DOWN DOWN DOWN 1112 CD74 UP DOWN UP UP 1113 LCK DOWN DOWN DOWN DOWN 1114 TLR4 UP UP UP UP 1115 DOWN DOWN DOWN DOWN 1116 DSC2 UP UP UP UP 1117 CXORF45 DOWN DOWN DOWN DOWN 1118 ENPP4 DOWN DOWN DOWN DOWN 1119 CD300C UP UP UP UP 1120 OASL DOWN UP UP UP 1121 HPSE UP UP UP UP 1122 MTHFD2 UP UP UP UP 1123 GSTM2 DOWN DOWN DOWN DOWN 1124 OLFM4 UP UP UP UP 1125 ABHD12B UP UP UP UP 1126 LOC728417 UP UP UP UP 1127 LOC728417 UP UP UP UP 1128 FCAR UP UP UP UP 1129 GTPBP3 DOWN DOWN DOWN DOWN 1130 KLF4 UP DOWN UP UP 1131 HOPX DOWN DOWN DOWN DOWN 1132 THBD UP UP DOWN UP 1133 HIST1H2BG DOWN UP DOWN UP 1134 LOC730995 DOWN DOWN DOWN DOWN 1135 OPN3 DOWN DOWN DOWN DOWN 1136 NOP56 DOWN DOWN DOWN DOWN 1137 ZBTB9 DOWN DOWN DOWN DOWN 1138 NLRC3 DOWN DOWN DOWN DOWN 1139 LOC100134083 UP UP UP UP 1140 COP1 UP UP UP UP 1141 CARD16 UP UP UP UP 1142 SP140 DOWN UP UP UP 1143 CD96 DOWN DOWN DOWN DOWN 1144 UBE2O DOWN DOWN UP DOWN 1145 POLD2 DOWN DOWN DOWN DOWN 1146 IL32 DOWN DOWN DOWN DOWN 1147 LOC728744 UP UP UP UP 1148 FZD2 UP UP UP UP 1149 ZAP70 DOWN DOWN DOWN DOWN 1150 PYHIN1 DOWN DOWN DOWN DOWN 1151 SCARF1 UP UP UP UP 1152 IFI27 UP UP UP UP 1153 PFKFB2 UP UP UP UP 1154 PAM UP UP DOWN DOWN 1155 WARS DOWN UP UP UP 1156 DOWN DOWN DOWN DOWN 1157 TCN1 UP UP UP UP 1158 LOC649839 DOWN DOWN DOWN DOWN 1159 MMP9 UP UP UP UP 1160 RIN3 UP UP UP UP 1161 TMEM194A DOWN DOWN DOWN DOWN 1162 TAP2 UP UP UP UP 1163 C17ORF87 DOWN DOWN UP UP 1164 LOC728650 UP UP UP UP 1165 PNMA3 DOWN DOWN DOWN DOWN 1166 CPT1B UP UP UP UP 1167 LTBP3 DOWN DOWN DOWN DOWN 1168 CCDC34 DOWN DOWN UP DOWN 1169 PRAGMIN DOWN DOWN DOWN DOWN 1170 C9ORF91 DOWN DOWN UP UP 1171 SMPDL3A UP UP UP UP 1172 GPR56 DOWN DOWN DOWN DOWN 1173 C14ORF147 UP UP UP UP 1174 SMARCD3 UP UP UP UP 1175 FAM119A DOWN DOWN DOWN DOWN 1176 LOC642334 UP UP UP UP 1177 ENOSF1 DOWN DOWN DOWN DOWN 1178 FAR2 UP UP UP UP 1179 LOC441763 UP UP DOWN UP 1180 TESC DOWN DOWN UP DOWN 1181 CECR6 UP UP UP UP 1182 KIAA1598 UP UP UP UP 1183 UP UP UP UP 1184 GPR109B UP UP UP UP 1185 LRRN3 DOWN DOWN DOWN DOWN 1186 RNF213 DOWN DOWN UP UP 1187 LRP3 UP UP UP UP 1188 ASGR2 UP UP UP UP 1189 ASGR2 UP UP UP UP 1190 ZSCAN18 DOWN DOWN DOWN DOWN 1191 MCOLN2 DOWN DOWN DOWN DOWN 1192 IFIT2 UP UP UP UP 1193 PLCH2 DOWN DOWN DOWN DOWN 1194 MAP7 DOWN DOWN DOWN DOWN 1195 GBP4 DOWN DOWN UP UP 1196 MGMT DOWN DOWN DOWN DOWN 1197 GAL3ST4 DOWN DOWN DOWN DOWN 1198 C2ORF89 DOWN DOWN DOWN DOWN 1199 TXNDC3 UP UP UP UP 1200 IFIH1 DOWN UP UP UP 1201 PRRG4 UP UP UP UP 1202 LOC641693 UP UP UP UP 1203 LOC728093 UP UP UP UP 1204 TNFAIP8L1 DOWN DOWN UP DOWN 1205 AP3M2 DOWN DOWN DOWN DOWN 1206 BACH2 DOWN DOWN DOWN DOWN 1207 BACH2 DOWN DOWN DOWN DOWN 1208 C9ORF123 DOWN DOWN DOWN DOWN 1209 CACNA1I DOWN DOWN DOWN DOWN 1210 LOC100132287 UP UP UP UP 1211 CAMK1D UP UP UP DOWN 1212 ANKRD33 UP UP UP UP 1213 CCR6 DOWN DOWN DOWN DOWN 1214 ALDH1A1 DOWN DOWN UP UP 1215 LOC100132797 DOWN UP DOWN DOWN 1216 CD163 UP UP UP UP 1217 ESAM UP UP UP UP 1218 FCAR UP UP UP UP 1219 TCN2 UP UP UP UP 1220 LOC100129203 DOWN DOWN DOWN UP 1221 CD6 DOWN DOWN DOWN DOWN 1222 B3GNT1 DOWN DOWN DOWN DOWN 1223 NEK8 DOWN DOWN DOWN DOWN 1224 SLC38A5 UP UP UP UP 1225 CD3E DOWN DOWN DOWN DOWN 1226 DOWN DOWN DOWN DOWN 1227 GPR183 DOWN DOWN DOWN DOWN 1228 CCDC76 DOWN DOWN DOWN DOWN 1229 MS4A1 DOWN DOWN DOWN DOWN 1230 IFIT1 DOWN UP UP UP 1231 MED13L UP UP DOWN DOWN 1232 SLC26A8 UP UP UP UP 1233 NOV DOWN DOWN DOWN DOWN 1234 FLJ20035 DOWN UP UP UP 1235 UGT1A3 UP UP UP UP 1236 LOC653600 UP UP UP UP 1237 LOC642684 UP UP UP UP 1238 KIAA0319L UP UP UP UP 1239 KLRD1 DOWN DOWN DOWN DOWN 1240 TRIM22 UP UP UP UP 1241 C4ORF18 UP UP UP UP 1242 TSPAN3 DOWN DOWN DOWN DOWN 1243 TSPAN3 DOWN DOWN DOWN DOWN 1244 LOC728748 DOWN DOWN DOWN DOWN 1245 DNAJC3 UP UP UP UP 1246 AGTRAP UP UP UP UP 1247 LOC646786 UP UP DOWN DOWN 1248 NCALD DOWN DOWN DOWN DOWN 1249 TTC25 DOWN DOWN UP DOWN 1250 LOC646966 DOWN DOWN DOWN DOWN 1251 TSPAN5 DOWN DOWN UP DOWN 1252 ZNF559 DOWN DOWN DOWN DOWN 1253 NFKB2 UP UP UP UP 1254 LOC652616 UP UP UP UP 1255 HLA-DOA DOWN DOWN UP DOWN 1256 WARS DOWN UP UP UP 1257 GBP2 UP UP UP UP 1258 AUTS2 DOWN DOWN DOWN DOWN 1259 IGF2BP3 UP UP UP UP 1260 OASL UP UP UP UP 1261 DYSF UP UP UP UP 1262 FLJ43093 DOWN DOWN UP DOWN 1263 FAM159A DOWN DOWN DOWN DOWN 1264 MS4A14 UP DOWN UP UP 1265 TGFB1I1 UP UP UP UP 1266 RAD51C DOWN DOWN DOWN DOWN 1267 CALD1 UP UP UP UP 1268 LOC441073 DOWN DOWN DOWN DOWN 1269 CCNC DOWN DOWN DOWN DOWN 1270 LOC730281 UP UP UP UP 1271 MUC1 UP UP UP UP 1272 C14ORF124 DOWN DOWN DOWN DOWN 1273 RPL14 DOWN DOWN DOWN DOWN 1274 APOL6 UP UP UP UP 1275 DOWN DOWN DOWN DOWN 1276 KCTD12 UP UP UP UP 1277 ITGAX UP UP UP UP 1278 IFIT3 UP UP UP UP 1279 LPCAT2 DOWN UP UP UP 1280 ZNF529 DOWN DOWN DOWN DOWN 1281 MRPL9 DOWN DOWN DOWN DOWN 1282 AGTRAP UP UP UP UP 1283 LOC402112 DOWN DOWN DOWN DOWN 1284 LOC100134822 UP UP UP UP 1285 SH2D1B DOWN DOWN DOWN DOWN 1286 MPO UP UP UP UP 1287 LOC100131967 UP UP UP UP 1288 LOC440459 UP UP UP UP 1289 FAM44B DOWN DOWN DOWN DOWN 1290 ACOT9 UP UP UP UP 1291 SLC37A1 DOWN UP UP UP 1292 LOC729915 UP UP UP UP 1293 PDZK1IP1 DOWN DOWN UP DOWN 1294 S100A12 UP UP UP UP 1295 RAB3IL1 DOWN DOWN UP UP 1296 TMEM204 DOWN DOWN DOWN DOWN 1297 CXCL10 UP UP UP UP 1298 TSR1 DOWN DOWN DOWN DOWN 1299 NSUN5 DOWN UP DOWN DOWN 1300 MXD3 UP UP UP UP 1301 LILRA5 UP UP UP UP 1302 CKAP4 UP UP UP UP 1303 C6ORF190 DOWN DOWN DOWN DOWN 1304 ECGF1 UP UP UP UP 1305 LDLRAP1 DOWN DOWN DOWN DOWN 1306 GRB10 UP UP UP UP 1307 FCRL3 DOWN DOWN DOWN DOWN 1308 LOC731275 UP UP UP UP 1309 ZFP91 UP UP DOWN UP 1310 CTRL UP UP UP UP 1311 BCL6 UP UP UP UP 1312 SAMD3 DOWN DOWN DOWN DOWN 1313 LOC647436 DOWN DOWN DOWN DOWN 1314 CLC DOWN DOWN UP DOWN 1315 GK UP UP UP UP 1316 LOC100133565 UP UP DOWN UP 1317 OAS2 UP DOWN UP UP 1318 LOC644937 DOWN DOWN DOWN DOWN 1319 SIRPD UP UP UP UP 1320 GPBAR1 UP DOWN UP UP 1321 GNL3 DOWN DOWN DOWN DOWN 1322 CD79B DOWN DOWN DOWN DOWN 1323 ELF2 UP UP UP UP 1324 GAA UP UP UP UP 1325 CD47 DOWN DOWN DOWN DOWN 1326 NMT2 DOWN DOWN DOWN DOWN 1327 MATR3 DOWN DOWN DOWN DOWN 1328 TMEM107 UP DOWN DOWN DOWN 1329 GCM1 UP UP UP UP 1330 RORA DOWN DOWN DOWN DOWN 1331 MGAM UP UP UP UP 1332 LOC100132491 UP UP UP UP 1333 KRT72 DOWN DOWN DOWN DOWN 1334 SEPT4 UP UP UP UP 1335 ACADVL UP UP UP UP 1336 ANXA3 UP UP UP UP 1337 MEGF9 UP UP UP UP 1338 MEGF9 UP UP UP UP 1339 PTPRJ UP UP UP UP 1340 HLA-DRB4 DOWN DOWN UP UP 1341 GHRL DOWN UP UP UP 1342 ALAS2 DOWN UP UP UP 1343 FFAR2 UP UP UP UP 1344 MPZL2 DOWN UP UP UP 1345 PML DOWN UP UP UP 1346 HLA-DQA1 DOWN DOWN UP UP 1347 CEACAM8 UP UP UP UP 1348 SH3KBP1 DOWN DOWN DOWN DOWN 1349 TRPM2 UP UP UP UP 1350 CUX1 UP UP UP UP 1351 LOC648390 DOWN DOWN UP DOWN 1352 SUV39H1 DOWN DOWN DOWN DOWN 1353 RNF13 UP UP UP UP 1354 USF1 UP UP UP UP 1355 VAPA UP UP UP UP 1356 ALOX15 DOWN DOWN UP DOWN 1357 CD79A DOWN DOWN DOWN DOWN 1358 DPRXP4 UP UP UP UP 1359 LOC652750 DOWN UP UP UP 1360 ECM1 UP UP DOWN UP 1361 ST6GAL1 DOWN DOWN DOWN DOWN 1362 KLHL3 DOWN DOWN DOWN DOWN 1363 RTP4 DOWN UP UP UP 1364 FAM179A DOWN DOWN UP DOWN 1365 HDC DOWN DOWN UP DOWN 1366 SUMO1P1 UP UP DOWN UP 1367 SACS DOWN DOWN DOWN DOWN 1368 C9ORF72 UP UP UP UP 1369 C9ORF72 UP UP UP UP 1370 LOC652726 DOWN DOWN DOWN DOWN 1371 PVRIG DOWN DOWN DOWN DOWN 1372 PPP1R16B DOWN DOWN DOWN DOWN 1373 NSUN7 UP UP DOWN DOWN 1374 NSUN7 UP UP DOWN UP 1375 UHRF2 DOWN DOWN DOWN DOWN 1376 ZNF783 DOWN DOWN DOWN DOWN 1377 LOC441013 DOWN DOWN DOWN DOWN 1378 UP UP UP UP 1379 LOC100129343 UP UP UP UP 1380 OSM UP UP UP UP 1381 UNC93B1 UP UP UP UP 1382 DNAJC30 DOWN DOWN DOWN DOWN 1383 FLJ14166 UP UP DOWN DOWN 1384 C9ORF72 UP UP DOWN UP 1385 SAMD4A UP UP UP UP 1386 RNY4 DOWN DOWN DOWN DOWN 1387 F5 UP UP UP UP 1388 PARP15 DOWN DOWN DOWN DOWN 1389 PAFAH2 DOWN DOWN DOWN DOWN 1390 COL17A1 UP UP UP UP 1391 LOC651524 UP UP UP UP 1392 TYMP UP UP UP UP 1393 LOC389672 DOWN DOWN DOWN DOWN 1394 ABCB1 DOWN DOWN DOWN DOWN 1395 LOC644852 DOWN DOWN UP UP 1396 TARP DOWN DOWN DOWN DOWN 1397 SLAMF7 UP UP UP UP 1398 FRMD3 UP UP UP UP 1399 LOC648984 UP UP UP UP 1400 PLAUR UP UP UP UP 1401 LOC100132119 UP UP UP UP 1402 KLRG1 DOWN DOWN DOWN DOWN 1403 INTS2 DOWN DOWN DOWN DOWN 1404 MYC DOWN DOWN DOWN DOWN 1405 HIST1H4H UP UP UP UP 1406 KBTBD8 DOWN DOWN DOWN DOWN 1407 C9ORF45 DOWN DOWN DOWN DOWN 1408 GBP6 UP UP UP UP 1409 KIFAP3 DOWN DOWN DOWN DOWN 1410 HSPC159 UP UP UP UP 1411 ZNF224 DOWN DOWN DOWN DOWN 1412 SOCS3 UP UP UP UP 1413 GOLGA8B DOWN DOWN DOWN DOWN 1414 OLIG1 DOWN DOWN UP DOWN 1415 TNFRSF4 DOWN DOWN UP DOWN 1416 LOC100133583 DOWN DOWN UP UP 1417 ARL4A DOWN DOWN DOWN DOWN 1418 ASNS DOWN DOWN DOWN DOWN 1419 ITGAX UP UP UP UP 1420 LOC153561 UP UP UP UP 1421 GSTM1 DOWN DOWN DOWN DOWN 1422 OAS2 DOWN DOWN UP UP 1423 OAS2 UP UP UP UP 1424 TRIM25 UP UP UP UP 1425 ABHD14A DOWN DOWN DOWN DOWN 1426 LOC642342 UP UP DOWN DOWN 1427 GPR56 DOWN DOWN DOWN DOWN 1428 C4ORF18 UP UP UP UP 1429 AK1 DOWN DOWN DOWN DOWN 1430 PIK3R6 DOWN UP UP UP 1431 HSPE1 DOWN DOWN DOWN DOWN 1432 ASPHD2 DOWN UP UP UP 1433 DHRS9 UP UP UP UP 1434 GRN UP UP UP UP 1435 BEND7 UP UP UP UP 1436 BOAT DOWN DOWN DOWN DOWN 1437 LOC728323 UP UP DOWN UP 1438 LOC100134300 UP UP UP UP 1439 SDSL UP UP UP UP 1440 TNFAIP6 UP UP UP UP 1441 ARHGAP24 UP UP UP UP 1442 LOC402176 UP UP UP DOWN 1443 LOC441019 DOWN DOWN UP UP 1444 FAM134B DOWN DOWN DOWN DOWN 1445 ZNF573 DOWN DOWN DOWN DOWN 1446

Distinct biological pathways were found to be associated with the pulmonary granulomatous diseases differing from those associated with the acute pulmonary diseases, pneumonias and chronic lung diseases, lung cancers.

Having established by the derived 1446-transcript signature that the pulmonary granulomatous diseases had similar transcriptional profiles to each other but different to those of the pneumonia and lung cancer patients we wished to determine the main biological pathways associated with the 1446-transcripts in relation to each disease (SEQ ID NOS.:1 to 1,446). The 1446 unsupervised clustering revealed three main clusters of transcripts as can be seen from the vertical dendrogram (FIG. 2). Ingenuity Pathway Analysis (IPA) of the main clusters of transcripts revealed that the TB and sarcoidosis samples were associated with over-abundance of the interferon signalling pathway and other immune response pathways (FIG. 2). However the pneumonia and lung cancer samples were associated with over-abundance of pathways linked with inflammation. All four diseases associated with under-abundance of T and B cell pathways. Using the 1,446 genes or probes, the skilled artisan can select subsets of genes that will best differentiate between two, three or four pulmonary diseases by taking advantage of both the level of expression but also whether the gene is over- or under-expressed. As taught herein, certain subsets are demonstrated to be unique to certain pulmonary diseases, but can also be used to identify if a patient or subject has one, two, three or four of the pulmonary diseases.

FIG. 2. Three dominant clusters of transcripts in the unsupervised clustering of the 1446 transcripts are associated with distinct Ingenuity Pathway Analysis canonical pathways. Each of the three dominant clusters of transcripts is associated with different study groups in the Training Set. The top transcript cluster is over-abundant in the pneumonia and lung cancer patients and significantly associated with IPA pathways relating to inflammation (Fisher's exact p<0.05 Benjamini Hochberg). The middle transcript cluster is over-abundant in the TB and sarcoidosis patients and significantly associated with interferon signalling and other immune response IPA pathways (Fisher's exact p<0.05 Benjamini Hochberg). The bottom transcript cluster is under-abundant in all the patients and significantly associated with T and B cell IPA pathways (Fisher's exact p<0.05 Benjamini Hochberg).

The sarcoidosis patients' heterogeneous transcriptional profiles were explained by their clinical phenotype.

From the unsupervised clustering of the 1446-transcripts it can be seen that the sarcoidosis patients fell into two groups, those that clustered with the TB patients and those that clustered with the healthy controls (FIG. 1). As the blood transcriptional profile is a snap shot view of the host's immune response we applied the same approach to clinically phenotyping the patients to understand if their clinical classification correlates with their transcriptional profile. However there is no consensus on how to reliably assess disease activity and current classification systems all require continuous follow-up of the patient over a prolonged period of time before their activity status can be stated (1). Therefore a clinical classification was devised decision tree based on clinical variables that are both routinely measure in sarcoidosis patients and have been shown to be associated with disease activity (data not shown). Using exactly the same analysis strategy as for the 1446-transcripts, but this time with the sarcoidosis patients classified as either active or non-active, 1396-transcripts were found to be differentially expressed across all the disease groups. FIGS. 3A and 3B shows the results from the sarcoidosis patients clinically classified as active sarcoidosis display similar transcriptional signatures to the TB patients but are very distinct from the transcriptional signatures of the clinically classified non-active sarcoidosis patients which in turn resemble the healthy controls. 1396-transcripts are differentially expressed in the whole blood of healthy controls, pulmonary TB patients, active sarcoidosis patients, non-active sarcoidosis patients, pneumonia patients and lung cancer patients. FIG. 3A shows the 1396 transcripts and Training Set patients' profiles are organised by unsupervised hierarchical clustering. A dotted line is added to the heatmap to clarify the main clusters generated by the clustering algorithm. Transcript intensity values are normalised to the median of all transcripts. FIG. 3B shows the molecular distance to health of the 1396 transcripts in the Training and Test sets demonstrates the quantification of transcriptional change relative to the controls. The mean and SEM was compared between each disease group (ANOVA with Tukey's multiple comparison test).

Unsupervised hierarchical clustering again showed the same clustering pattern as seen with the 1446-transcripts (FIG. 3A). Applying the clinical classification decision tree it could be seen that those sarcoidosis patients clustering with the TB patients had been classified as active and those with the healthy controls as non-active. This was further validated in two independent cohorts, the Test and Validation Sets (data not shown). In addition, it was found that the applied clinical classification decision tree was able to predict if the sarcoidosis patients' transcriptional profiles clustered with the TB patients or the healthy controls better than any routinely measured single clinical variable (data not shown). Furthermore the clinical classification decision tree was still superior in its clustering predictive ability even if the single clinical variables with the highest predictive values were used in conjunction with each other or even when used together with the clinical classification criteria (data not shown). Molecular distance to health (MDTH) demonstrates the quantification of transcriptional change relative to the controls (FIG. 3B) (2). By applying this algorithm to all the disease groups for the 1396-transcripts it could be seen that the non-active sarcoidosis MDTH score was not significantly different from the controls, however the active sarcoidosis MDTH score was significantly different from the controls. In addition the TB patients' MDTH score was significantly higher than active sarcoidosis patients' score. Lung cancer and pneumonia both had significantly higher scores than the controls with pneumonia significantly higher than cancer. Pneumonia and TB had the highest MDTH scores. The significant differences in the MDTH scores between the patient groups suggest there is a quantitative as well as qualitative difference in blood transcriptional signatures between these similar pulmonary diseases.

Three different data mining strategies showed the same findings that both TB and active sarcoidosis were dominated by IFN-inducible genes, in contrast to pneumonia and lung cancer, which were dominated by inflammatory genes.

To further understand the biological pathways associated with each disease group we undertook three different data mining strategies to ensure our findings were robust and consistent. The three approaches applied were: modular analysis, Ingenuity Pathway Analysis and annotation of the top differentially expressed genes for each disease group.

To carry out modular analysis all detectable genes (15,212 transcripts) in the whole Training set dataset were analysed. Each module corresponds to a set of co-regulated genes that were assigned biological functions by unbiased literature profiling (3). FIGS. 4A to 4E shows modular analysis of the Training Set shows the similarity of the biological pathways associated with TB and sarcoidosis (particularly overexpression of the IFN modules), differing from pneumonia and lung cancer (particularly overexpression of the inflammation modules). FIG. 4A shows gene expression levels of all transcripts that were significantly detected compared to background hybridisation (15,212 transcripts, p<0.01) were compared in the Training Set between each patient group: TB, active sarcoidosis, non-active sarcoidosis, pneumonia, lung cancer, to the healthy controls. Each module corresponds to a set of co-regulated genes that were assigned biological functions by unbiased literature profiling. A red dot indicates significant over-abundance of transcripts and a blue dot indicates significant under-abundance (p<0.05). The colour intensity correlates with the percentage of genes in that module that are significantly differentially expressed. The modular analysis can also be represented in graphical form as shown in 4B-E, including both the Training and Test Set samples. FIG. 4B shows the percentage of genes significantly overexpressed in the 3 IFN modules for each disease. FIG. 4C shows the fold change of the expression of the genes present in the IFN modules compared to the controls. FIG. 4D shows the percentage of genes significantly overexpressed in the 5 inflammation modules for each disease. FIG. 4E shows the fold change of the expression of the genes present in the inflammation modules compared to the controls. TB and active sarcoidosis show significant overexpression of the IFN modules compared to the other pulmonary disease groups (FIG. 4A). In contrast the pneumonia and cancer patients showed significant overexpression of the inflammation modules compared to TB and active sarcoidosis. These findings were then verified by modular analysis of the Test Set (Figure E7). The modular analysis therefore also substantiates our results determined from pathways linked to the 1446-transcripts signature described earlier (FIG. 2). TB patients showed a significant increase in the number of IFN genes (FIG. 4B), and their degree of expression (FIG. 4C), compared to the active sarcoidosis patients, demonstrating a quantitative difference in the IFN-inducible signature between TB and active sarcoidosis (FIG. 4B-C) The same genes in the IFN module that were overexpressed in the active sarcoidosis patients were also overexpressed in the TB patients (data not shown). Pneumonia and lung cancer showed a significant increase in the number of genes present in the inflammation modules (FIG. 4D), and their degree of expression (FIG. 4E), in comparison to TB and active sarcoidosis (FIG. 4A, D-E). Pneumonia patients also showed a significant overexpression of the number of genes present in the neutrophil module compared to all the other pulmonary diseases (Figure E8). Whole blood gene expression may correlate with the blood's cell composition or with the gene expression in particular cellular populations. For the neutrophil genes there was a significant correlation between the neutrophil module and the neutrophil count for all the pneumonia patients versus controls (Pearson's correlation, p<0.0001). The second data mining approach, comparison IPA, only used those genes that were differentially expressed between each disease group and a set of controls matched by ethnicity and gender (≧1.5 fold change from the mean of the controls, Mann Whitney Benjamini Hochberg p<0.01; TB=2524, active sarcoidosis=1391, pneumonia=2801 and lung cancer=1626 differentially expressed transcripts). FIG. 5A shows a comparison Ingenuity Pathway Analysis of the four disease groups compared to their matched controls reveals the four most significant pathways. Differentially expressed genes were derived from the Training Set by comparing each disease to healthy controls matched for ethnicity and gender: TB=2524, active sarcoidosis=1391, pneumonia=2801 and lung cancer=1626 transcripts (≧1.5 fold change from the mean of the controls, Mann Whitney Benjamini Hochberg p<0.01). FIG. 5A shows the IPA canonical pathways was used to determined the most significant pathways (i-iv) associated with each disease relative to the other diseases (Fisher's exact Benjamini Hochberg). The bottom x-axis and bars of each graph indicates the log(p-value) and the top x-axis and line indicates the percentage of genes present in the pathway. The genes in the EIF2 signalling pathway are predominately under-abundant genes however the genes in the other three pathways are predominantly over-abundant relative to the controls. Pathways above the blue dotted line are significant (p<0.05). FIGS. 5B, 5C and 5D show the interferon signalling IPA pathway is overlaid onto each disease group. Coloured genes are differentially expressed in that disease group compared to their matched controls (Fisher's exact p<0.05). Red genes represent over-abundance and green under-abundance.

The Comparison IPA reveals the most significant pathways when comparing across the diseases. The top four significant pathways were related to protein synthesis (EIF2 signalling) and immune response pathways (interferon signalling, role of pattern recognition receptors in recognition of bacteria and viruses and antigen presentation pathway)(FIG. 5A). The prominence of the EIF2 signalling pathway was driven by the pneumonia patients. The genes were significantly under-abundant in the pneumonia patients compared to the other pulmonary diseases. Many other genes related to protein synthesis (including eukaryotic initiation factors and ribosomal proteins) and the unfolded protein response (a stress response to excessive protein synthesis), were also significantly under-abundant in the pneumonia patients compared to the other pulmonary diseases, e.g. PERK, CHOP, ABCE1 (data not shown). The significance of the three immune response pathways was driven predominantly by the TB patients, but also by the sarcoidosis patients. The pathways were more significant (bottom x-axis bar graph in FIG. 5A) and contained a higher number of genes (top x-axis line graph in FIG. 5A) in both TB and active sarcoidosis than compared to the other pulmonary diseases, again demonstrating the similarity of the biological pathways underlying these pulmonary granulomatous diseases. However the interferon signalling pathway was more significant (bottom x-axis bar graph FIG. 5A) and contained a higher number of genes in the TB than the active sarcoidosis patients and were not represented in pneumonia and lung cancer (top x-axis line graph FIG. 5A, FIG. 5B and FIG. 5C).

The third data mining strategy just examined the top 50 over-abundant differentially expressed transcripts for each disease. It could be seen that the transcripts correlate well with the findings from the modular and IPA analysis as both the TB and active sarcoidosis top 50 over-abundant transcripts were dominated by IFN-inducible genes e.g. IFITM3 (SEQ ID NO.:989), IFIT3 (SEQ ID NO.:1279), GBP1 (SEQ ID NO.:226), GBP6 (SEQ ID NO.:1409), CXCL10 (SEQ ID NO.:1298), OAS1 (SEQ ID NO.:790), STAT1 (SEQ ID NO.:995), IFI44L (SEQ ID NO.:1013), FCGR1B (SEQ ID NO.:63) (Table 6). However the expression fold change was much higher in the TB patients than the active sarcoidosis patients. In addition the pneumonia top 50 over-abundant transcripts were dominated by antimicrobial neutrophil-related genes e.g., ELANE (SEQ ID NO.:330), DEFA1B (SEQ ID NO.:1024), MMP8 (SEQ ID NO.:521), CAMP (SEQ ID NO.:40), DEFA3 (SEQ ID NO.:1088), DEFA4 (SEQ ID NO.:231), MPO (SEQ ID NO.:1287), LTF (SEQ ID NO.:506). The genes FCGR1A, B and C ((SEQ ID NO.:1109, 63, 50, respectively)) were over-abundant in the top 50 transcripts of all four pulmonary diseases. A 4-set Venn diagram of the differentially expressed genes was able to demonstrate the unique genes for each disease group (FIG. 9 and Table 7). There were over three times the number of unique TB genes than unique active sarcoidosis genes of which only the TB unique genes were significantly associated with the IPA IFN-signalling pathway. The unique pneumonia genes were associated with an under-abundance of pathways related to protein synthesis. The unique lung cancer genes were associated with over-abundance of inflammation related pathways. The overlapping genes common to all four disease groups were significantly associated with under-abundance of T and B cell pathways.

TB and pneumonia patients after treatment showed a diminishment of their transcriptional profiles to resemble the controls however the sarcoidosis patients who respond to glucocorticoids showed a significant increase in their transcriptional activity.

FIGS. 6A to 6D show both modular analysis and molecular distance to health reveal that the blood transcriptome of the pneumonia and TB patients after successfully completing treatment are no different from the healthy controls however the sarcoidosis patients show an overexpression of inflammation genes during a clinically successful response to glucocorticoids. FIG. 6A shows a modular analysis for gene expression levels of all transcripts that were significantly detected compared to background hybridisation (p<0.01) were compared between the healthy controls and each of the following the patient groups: pre-treatment pneumonia, post-treatment pneumonia patients and pre-treatment sarcoidosis, inadequate treatment response sarcoidosis and good treatment response sarcoidosis patients. A red dot indicates significant over-abundance of transcripts and a blue dot indicates under-abundance (p<0.05). The colour intensity correlates with the percentage of genes in that module that are significantly differentially expressed. MDTH demonstrates the quantification of transcriptional change after treatment in the 1446-transcripts relative to controls for pre-treatment pneumonia, post-treatment pneumonia patients, pre-treatment TB and post-treatment TB and and pre-treatment sarcoidosis, inadequate treatment response sarcoidosis and good treatment response sarcoidosis patients. The mean and SEM was compared between each disease group (ANOVA with Tukey's multiple comparison test). FIG. 6B, Pneumonia patients; FIG. 6C, TB patients from the Bloom et al, 2012 (12), study carried out in South Africa, the controls in this study were participants with latent TB; FIG. 6D Sarcoidosis patients.

More specifically, having determined the blood transcriptional signatures of untreated patients with the pulmonary granulomatous diseases TB and sarcoidosis and the infectious disease community and acute lung diseases of acquired pneumonia we next sought to examine their transcriptional response to treatment. The pneumonia patients were all followed-up at least 6 weeks after their hospital discharge and showed a good clinical response to their treatment with standard antibiotics (clinical data not shown but available). Using two completely different data mining strategies, modular analysis (all detectable transcripts were analysed) and MDTH (only the 1446-transcripts were analysed), it could be seen that the pneumonia patients after successful treatment showed a reversal of their transcriptional profiles such that there was no significant difference between the pneumonia post-treatment transcriptional profiles and the healthy controls (FIGS. 6A & B). We have previously studied the blood transcriptional response of a cohort of active TB patients from South Africa before and after successful anti-TB treatment (4). Therefore we used the same 1446-transcripts that were derived from this present study to assess the transcriptional response of these South African TB patients before and after treatment, compared to their latent TB controls. The MDTH score of the untreated active TB patients were significantly different from the latent TB controls however the transcriptional response after treatment again reversed with no significant difference between the treated active TB patients and the latent TB controls (FIG. 6C).

The treated sarcoidosis patients showed a variable clinical response after immunosuppressive treatment initiation as determined by their practising physician (clinical data not shown but available). If the physician increased their treatment at their clinic follow-up the patient was categorised as having an ‘inadequate treatment response’ but if the physician continued the same treatment or reduced their treatment this was categorised as having a ‘good treatment response’. Applying the same two data mining strategies as used for the pneumonia patients it could clearly be seen that the sarcoidosis patients who had a good clinical response to glucocorticoids had a significant overexpression of inflammatory genes that was not seen when the same or the different sarcoidosis patients had an inadequate response to immunosuppressive treatment (FIGS. 6A & D). The majority of the inflammatory genes that were overexpressed in the untreated pneumonia and lung cancer patients were also overexpressed in the good-treatment response sarcoidosis patients (Table 8), but many more transcripts were overexpressed in the good-treatment response sarcoidosis patients (clinical data not shown but available). The term inflammation comprises many forms and therefore there is a diversity of genes that are called inflammatory. Interestingly many of the top 50 overexpressed inflammatory genes in the good-treatment response sarcoidosis patients are known to be anti-inflammatory genes which are invariably induced alongside proinflammatory genes in what is termed an inflammatory response, e.g., IL1R2 (SEQ ID NO.:1007), DUSP1, IL18R (SEQ ID NO.:239), C-FOS, IκBα and MAPK1, as well as pro-inflammatory genes (Table 8).

The interferon-inducible genes were most abundant in the neutrophils in both TB and sarcoidosis. It was previously shown in the Berry, et al., 2010 publication (5) that the active TB signature was dominated by a neutrophil-driven IFN-inducible gene profile, consisting of both IFN-γ and type I IFN-αβ signalling (5). Therefore the inventors identified the main cell populations driving the IFN-inducible signature in the active sarcoidosis patients. A new cohort of patients (TB and active sarcoidosis) were recruited and controls to test the same IFN-inducible genes as used in the Berry, et al., 2010 publication (5) in the purified leucocyte populations of TB and sarcoidosis patients who had an IFN-inducible signature present in whole blood (Table 9).

FIGS. 7A to 7E show that interferon-inducible gene expression is most abundant in the neutrophils in both TB and sarcoidosis. The expression of interferon-inducible genes was measured in purified leucocyte populations from whole blood. FIG. 7A is a heatmap that shows the expression of IFN-inducible transcripts, from the Berry, et al., 2010 study (5), for each disease group normalised to the controls for that cell type. FIG. 7B shows the expression fold change in the TB samples of the same IFN-inducible transcripts. FIG. 7C shows the expression fold change in the sarcoidosis samples of the same IFN-inducible transcripts. FIG. 7D shows the expression fold change in the TB samples of all the genes present in the three interferon modules compared to the controls. FIG. 7E shows the expression fold change in the sarcoidosis samples of all the genes present in the three interferon modules compared to the controls.

Again the neutrophils displayed the highest relative abundance of IFN-inducible genes in active TB (FIGS. 7A, 7B & 7D). The neutrophils also had the highest abundance of IFN-inducible genes in the sarcoidosis patients, although to a lesser extent than was seen in the TB patients (FIGS. 7A, 7C & 7E). The monocytes showed a higher abundance of IFN-inducible genes than the lymphocytes in both the TB and sarcoidosis patients (FIG. 7A-E), as previously shown (5).

FIG. 8 shows the results for each of the pulmonary diseases using the genes expressed in a neutrophil module. FIG. 8A shows the percentage of genes significantly overexpressed in the neutrophil module for each disease in both the Training and Test set. FIG. 8B shows the fold change of the expression of the genes present in the neutrophil module compared to the controls.

FIG. 9 is a 4-set Venn diagram comparing the differentially expressed genes for each disease group compared to their ethnicity and gender matched controls. Differentially expressed genes were derived from the Training Set by comparing each disease to healthy controls matched for ethnicity and gender: TB=2524, active sarcoidosis=1391, pneumonia=2801 and lung cancer=1626 transcripts (≧1.5 fold change from the mean of the controls, Mann Whitney Benjamini Hochberg p<0.01). The 4-set Venn diagram was created using Venny (13). IPA canonical pathways was used to determined the most significant pathways associated with the unique transcripts for each disease (Fisher's exact p<0.05). Active Sarc=active sarcoidosis.

FIG. 10A is a Venn diagram comparing the gene lists used in the class prediction. The gene lists were obtained from this study (144 Illumina probes), Maertzdorf, et al., study (8) (100 Agilent probes of which only 76 probes were recognised as genes using NIH DAVID Gene ID Conversion Tool) and Koth, et al., study (7) (50 genes obtained from a Affymetrix platform although analysis also included data obtained from alternative studies from GEO databases which used other microarray platforms the majority from the Berry et al, 2010 (5) by current applicants). In the Illumina platform used to compare these lists some genes are represented by more than one transcript for example the 50 genes in Koth et al study (7) translate to 77 Illumina probes/transcripts.

144-transcripts were able to distinguish with good sensitivity and specificity the TB patients from the other pulmonary diseases and healthy controls.

Although the transcriptional profiles of the TB and active sarcoidosis patients appeared very similar we wished to determine if a gene list could distinguish the TB samples, from all the other patient and control samples. Therefore we compared the TB transcriptional profiles to the most similar group, active sarcoidosis, to derive a set of differentially expressed genes. 144 transcripts were differentially expressed between the TB and active sarcoidosis transcriptional profiles from the Training Set (significance analysis of microarray q<0.05, fold change≧1.5). Many of the transcripts were IFN-inducible genes and were all over-abundant in the TB profiles compared to the active sarcoidosis profiles (Table 2). Two recent publications also described gene lists that could distinguish TB from all sarcoidosis patients (7, 8). These previously published gene lists were derived from different cohorts and used different microarray platforms. We used a class prediction machine learned algorithm, support vector machines (SVM), to test our gene list and the two previously published gene lists for their ability to predict whether a transcriptional profile belonged to a TB patient or not. The prediction model is built using the transcriptional signature from samples with known disease-types to predict the classification of a new collection of samples. The SVM model should therefore be built in one study cohort and run in an independent cohort to prevent over-fitting the predictive signature. This was possible for all our cohorts. Where the study cohorts used a different microarray platform the SVM model had to be re-built in that cohort. However to reduce the effects of over-fitting the same parameters were used every time the SVM model was built.

TABLE 2 144 transcripts. The 144 transcripts are differentially expressed genes between the TB and active sarcoidosis profiles in the Training Set (significance analysis of microarray q < 0.05, fold change ≧ 1.5). Fold Change TB vs Active Symbol Sarcold Regulation C1QB 10.6 UP LOC100133565 6.4 UP TDRD9 5.3 UP ABCA2 5.3 UP SMARCD3 5.3 UP CACNA1E 5.1 UP HP 4.2 UP NTN3 4.2 UP LOC100008589 3.3 UP CARD17 3.3 UP LOC441763 3.2 UP ERLIN1 3.1 UP SLPI 3.1 UP SLC26AB 2.9 UP AIM2 2.8 UP INCA 2.8 UP OPLAH 2.7 UP LPCAT2 2.6 UP SEPT4 2.5 UP DISC1 2.5 UP 2FP91 2.5 UP UBE2J2 2.4 UP KREMEN1 2.4 UP ALPL 2.3 UP LOC100008589 2.3 UP KCN816 2.2 UP C19orf59 2.2 UP FCGR1A 2.2 UP SPATA13 2.2 UP ADM 2.2 UP CDKSRAP2 2.2 UP SNORA73B 2.2 UP TncRNA 2.1 UP PPAP2C 2.1 UP IFITM3 2.1 UP FCGR1B 2.1 UP JMJD6 2.1 UP HIST2H3D 2.1 UP LMNB1 2.0 UP S100A12 2.0 UP FCGR1C 2.0 UP LOC653591 2.0 UP LOC100132394 2.0 UP SLC26A8 2.0 UP ANXA3 2.0 UP NLRC4 1.9 UP LOC100134364 1.9 UP LILRA6 1.9 UP LOC653610 1.9 UP CST7 1.9 UP LILRB4 1.9 UP MSL3L1 1.9 UP HIST2H2BG 1.9 UP OSM 1.9 UP LILRAS 1.9 UP GPR97 1.9 UP HIST2H2AC 1.9 UP LILRAS 1.8 UP TLR5 1.8 UP LOC728417 1.8 UP MSL3 1.8 UP HPSE 1.8 UP RGL4 1.8 UP CYP1B1 1.8 UP HIST2H2AA3 1.8 UP AGTRAP 1.8 UP PFKB3 1.8 UP GNG8 1.8 UP LIB4R 1.8 UP H2AFJ 1.8 UP LILRA5 1.8 UP ABCA1 1.8 UP SULT1B1 1.8 UP GYG1 1.7 UP IFITM1 1.7 UP SVIL 1.7 UP DGAT2 1.7 UP MEFV 1.7 UP PIM3 1.7 UP MTRF1L 1.7 UP MAZ 1.7 UP HIST2H2AA4 1.7 UP LOC728519 1.7 UP SMARCD3 1.7 UP LOC641710 1.7 UP HIST2H2BE 1.7 UP ITPRIPL2 1.7 UP FXBP5 1.7 UP IFNAR1 1.6 UP LY96 1.6 UP LOC728417 1.6 UP DHRS13 1.6 UP IL18R1 1.6 UP GPR109B 1.6 UP AGTRAP 1.6 UP GPR1D9A 1.6 UP PLAC8 1.6 UP BAGE5 1.6 UP DUSP3 1.6 UP SLC22A4 1.6 UP LOC645159 1.6 UP 1L4R 1.6 UP FLI32255 1.6 UP HIST2H2AA3 1.6 UP PLAC8 2.6 UP SH3GLB1 1.6 UP PLSCR1 1.6 UP IFI35 1.6 UP TAOK1 1.6 UP MCTP1 1.6 UP CEACAM1 1.6 UP B4GALT5 1.6 UP COP1 1.6 UP PROK2 1.6 UP IFI30 1.6 UP FCER1G 1.5 UP 2NF438 1.5 UP EEF1D 1.5 UP MIR21 1.5 UP NGFRAP1 1.5 UP PGS1 1.5 UP KIF1B 1.5 UP C16orf57 1.5 UP ANKRD33 1.5 UP MXD4 −1.5 DOWN 2SCAN18 −1.6 DOWN MEF2D −1.6 DOWN BHLHB2 −1.7 DOWN CLC −2.3 DOWN FCER1A −2.5 DOWN SRGAP3 −2.6 DOWN FLI43093 −2.8 DOWN CCR3 −2.9 DOWN EMR4 −3.0 DOWN ZNf792 −3.1 DOWN C10orf33 −3.5 DOWN CACNG6 −3.8 DOWN P2RY10 −4.2 DOWN GATA2 −4.6 DOWN EMR4P −6.6 DOWN ESPN −7.0 DOWN EMR4 −9.3 DOWN

The 144 Illumina transcripts showed good sensitivity (above 80%) and specificity (above 90%) in all three independent cohorts from our study (Training, Test and Validation Sets) and when using an external cohort from the Maertzdorf et al study. The 100 Agilent transcripts from the Maertzdorf et al 2012 study were also tested (7). Only 76 of these transcripts were recognised as genes by NIH DAVID Gene ID Conversion Tool. The same SVM parameters as used earlier were then applied using the Maertzdorf et al transcripts in our three independent cohorts (Training, Test and Validation Sets). The sensitivity however was much lower (45-56%), with similar specificity (above 90%). The 50 genes from the Koth et al 2011 (7) study run using an Affymetrix platform were also tested. The same SVM parameters were again applied to all our independent cohorts (Training, Test and Validation Sets). The sensitivity of this gene list was also lower (75-45%), with similar specificity (above 87%), than for our 144-transcripts. Neither the Koth et al 2011 (7) or the Maertzdorf et al 2012 (8) studies reported testing their derived gene lists in independent cohorts. As these study tested the 144-transcripts list from the present applicants (Bloom, O'Garra et al., to be submitted), in both internal and external independent cohorts this is likely to have improved the validity of the transcript list as a discriminative marker, and may explain why there was little overlap between their gene lists or overlap with the present applicants' 144 gene list (Figure E10). Tables 3, 4 and 5. Class prediction. Class prediction was performed using support vector machines (SVM).

Table 2 (above) shows the 144 transcripts derived from the Training Set which were then used to build the SVM model, the model was then run in the other four cohorts Table 3 (just below).

The 144 transcripts derived from the Training Set in this present study, Bloom et al (Illumina), were tested in the cohorts below: Present study Present study Training Set Test Set Maertzdorf (controls, TB, (controls, TB, Present study et al sarcoid, sarcoid, Validation Set controls, cancer, cancer, (controls, TB, TB, pneumonia) pneumonia) sarcoid) (sarcoid) Sensitivity 88% 82% 88% 88% Specificity 94% 91% 92% 97%

Table 4 (below). The 100 Agilent transcripts from the Maertzdorf et al study (8) translated to 76 recognised genes using the DAVID gene converter. The SVM model was built in the Training Set and run in the Test and Validation Sets.

The 76 recognised genes out of the 100 probes from the Maertzdorf et al study (Agilent) were tested in the cohorts below: Present study Present study Maertzdorf Training Set Test Set Present study et al (controls, TB, (controls, TB, Validation Set (controls, sarcoid, cancer, sarcoid, cancer, (controls, TB, TB, pneumonia) pneumonia) sarcoid) sarcoid) Sensitivity 56% 45% 75% 88% (as stated in their publication) Specificity 96% 92% 92% 97% (as stated in their publication)

Table 5 (below) shows the 50 genes from the Koth et al study (7) were used to build the last SVM model in the Training Set and run in the Test and Validation Sets. N/A=not applicable.

The 50 genes from the Koth et al study (Affymetrix) were tested in the cohorts below: Present study Present study Koth et al Training Set Test Set Present study (sarcoid and (controls, TB, (controls, TB, Validation Set all cohorts sarcoid, cancer, sarcoid, cancer, (controls, TB, from Berry pneumonia) pneumonia) sarcoid) et al study) Sensitivity 75% 45% 50% Not shown in their publication Specificity 92% 87% 92% Not shown in their publication

Table 6 (below). The top 50 differentially expressed transcripts for each disease compared to matched controls (from the present applicants' study). Differentially expressed genes were derived from the Training Set by comparing each disease to healthy controls matched for ethnicity and gender: TB=2524, active sarcoidosis=1391, pneumonia=2801 and lung cancer=1626 transcripts (≧1.5 fold change from the mean of the controls, Mann Whitney Benjamini Hochberg p<0.01).

TB Active sarcoidosis Pneumonia Lung cancer Fold Change Gene Symbol Fold Change Symbol Fold Change Gene Symbol Fold Change Gene Symbol 21 ANKRD22 8.1 FCGR1A 15.8 OLFM4 6.1 ARG1 18.5 FCGR1A 7.9 ANKRD22 12.7 LTF 5.5 TPST1 17.4 SERPING1 7.4 FCGR1C 12.6 VNN1 5.4 FCGR1A 15.1 BATF2 7.1 FCGR1B 12.4 HP 5.2 C19orf59 14.9 FCGR1C 6.4 SERPING1 12.3 DEFA4 4.6 SLPI 13.7 FCGR1B 6.2 FCGR1B 11.3 OPLAH 4.5 FCGR1B 13.3 ANKRD22 6 BATF2 11.2 CEACAM8 4.3 IL1R1 13.1 FCGR1B 5.5 GBP5 11 DEFA1B 4.1 FCGR1C 10.8 LOC728744 5.3 GBP1 10.1 ELANE 4.1 TDRD9 10 IFITM3 5.1 IFIT3 9.4 C19orf59 4.1 SLC26A8 9.5 EPSTI1 5 ANKRD22 9.2 ARGI 4.1 FCGR1B 8.7 GBPS 4.9 LOC728744 8.7 CDK5RAP2 4.1 CLEC4D 8.7 IFI44L 4.8 GBP1 8.6 DEFA1B 4 LOC100132858 8.4 GBP6 4.8 EPSTI1 8.4 DEFA3 3.9 SLC22A4 8.1 GBP1 4.6 IFI44L 8.3 DEFA1B 3.8 LOC100133177 7.8 LOC400759 4.6 INDO 8.1 FCGR1A 3.7 SIPA1L2 7.7 IFIT3 4 IFITM3 7.9 MMP8 3.6 ANXA3 7.6 AIM2 4 GBP6 7.4 FCGR1B 3.6 LIMK2 7.3 SEPT4 4 RSAD2 7.3 SLPI 3.5 TMEM88 7.1 C1QB 3.9 DHRS9 7.2 SLC26A8 3.5 MMP9 6.9 GBP1 3.7 TNFAIP6 7.1 MAPK14 3.5 ASPRV1 6.9 RSAD2 3.7 IFIT3 7.1 CAMP 3.5 MANSC1 6.4 RTP4 3.5 P2RY14 6.7 NLRC4 3.5 TLR5 6.1 CARD17 3.4 DHRS9 6.4 FCAR 3.5 CD163 5.9 IFIT3 3.4 IDO1 6.3 RNASE3 3.4 CAMP 5.6 CASP5 3.3 STAT1 6.3 FCGR1B 3.4 LOC642816 5.4 CEACAM1 3.3 WARS 6.2 NAIP 3.4 DPRXP4 5.4 CARD17 3.2 TIMM10 6.2 OLR1 3.4 LOC643313 5.3 ISG15 3.1 P2RY14 6.1 FCGR1C 3.3 NTN3 5.2 IFI27 3.1 LOC389386 6.1 ANXA3 3.3 MRVI1 5.1 TIMM10 3.1 FERIL3 6 DEFAI 3.3 F5 5 WARS 3 IFIT3 6 PGLYRP1 3.3 SOCS3 4.8 IFI6 3 RTP4 6 TCN1 3.3 TncRNA 4.7 TNFAIP6 3 SCO2 6 ANKDD1A 3.3 MIR21 4.7 PSTPIP2 3 GBP4 5.8 COL17A1 3.2 LOC100170939 4.7 IFI44 2.9 IFIT1 5.8 SLC26A8 3.2 LOC100129904 4.6 SCO2 2.9 LAP3 5.8 IMEM144 3.2 GRB10 4.6 FBXO6 2.9 OASL 5.8 SAMD14 3.2 ASGR2 4.5 FER1L3 2.9 CEACAM1 5.8 MAPK14 3.2 LOC642780 4.5 CXCL10 2.9 LIMK2 5.7 RETN 3.2 LOC400499 4.3 DHR59 2.8 CASP5 5.7 NAIP 3.1 FCAR 4.3 OAS1 2.8 STAT1 5.7 GPR84 3.1 KREMENI 4.3 STAT1 2.8 CCL23 5.6 CASP5 3.1 SLC2ZA4 4.2 HP 2.8 WARS 5.6 MPO 3.1 CR1 4.2 DHR59 2.7 ATF3 5.6 MMP9 3.1 LOC730234 4.2 CEACAM1 2.7 IFI6 5.5 CR1 3.1 SLC26A8 4.2 SLC26A8 2.7 PSTPIP2 5.4 MYL9 3.1 C7orf53 4.2 CACNA1E 2.7 ASPRVI 5.2 CLEC4D 3.1 VNN1 4.1 OLFM4 2.7 FBXO6 5.1 ITGAX 3.1 NLRC4 4.1 APOL6 2.7 CXCL10 5.1 ANKRD22 3.1 LOC400499

Table 7 (below). The top 50 differentially expressed transcripts unique for each disease as determined by the 4-set Venn diagram (from the present applicants study). Differentially expressed genes were derived from the Training Set by comparing each disease to healthy controls matched for ethnicity and gender (≧1.5 fold change from the mean of the controls, Mann Whitney Benjamini Hochberg p<0.01). A 4-set Venn diagram was used to identify genes that were unique for each disease.

TB Sarcoidosis Pneumonia Lung Cancer Fold Change Gene Symbol Fold Change Gene Symbol Fold Change Gene Symbol Fold Change Gene Symbol 7.1 C1QB 2.8 CCL23 12.3 DEFA4 5.5 TPST1 5.2 IFI27 2.1 PIK3R6 10.1 ELANE 3.3 MRVI1 3.5 SMARCD3 2.1 EMR4 7.9 MMP8 3.1 C7orf53 3.2 SOCS1 2.0 CCDC146 6.2 OLR1 3.0 ECHDC3 3.1 KCNI15 2.0 KLF4 5.8 COLI7A1 2.9 LOC651612 2.9 LPCAT2 2.0 GRINA 5.7 RETN 2.9 LOC100134660 2.8 ZDHHC19 1.9 SLC4A1 5.7 GPR84 2.8 TIAM2 2.8 FYB 1.9 PLA2G7 4.6 LOC100134379 2.8 KIAA1026 2.8 SP140 1.9 GRAMD1B 4.5 TACSTO2 2.8 HECW2 2.6 IFITM1 1.9 RAPGEF1 4.0 SLC2A11 2.7 TLE3 2.6 ALAS2 1.8 NXNL1 3.9 LOC100130904 2.7 TBC1D24 2.6 CEACAM6 1.8 TRIM5B 3.8 MCTP2 2.7 LOC441193 2.6 OAS2 1.8 GABBR1 3.7 AZU1 2.7 CD163 2.5 C1QC 1.7 TAGLN 3.6 DACH1 2.6 RFX2 2.5 LOC100133565 1.7 KLI4 3.6 GADD45A 2.6 LOC100134688 2.5 ITGA2B 1.7 MFAP3L 3.6 NSUN7 2.4 LOC642342 2.4 LY66 1.7 LOC641798 3.5 CR1 2.3 FXBP9L 2.4 SP140 1.7 RIPK2 3.4 CDK5RAP2 2.3 PHF2DL1 2.4 CASP7 1.7 LOC650840 3.3 LOC284648 2.3 LOC402176 2.4 GADD45G 1.7 FLI43093 3.1 GPR177 2.3 CD163 2.3 FRMD3 1.7 ASAP2 3.1 CLECSA 2.3 OSBPL1A 2.3 CMPK2 1.7 C15orf26 3.1 UPB1 2.3 PRMT5 2.3 AQP10 1.7 REC8 3.1 SLC2A5 2.3 LIBTD1 2.3 CXCL14 1.7 KIAA0319L 3.1 GPR177 2.3 ADORA3 2.3 I7PRIPL2 1.7 GRINA 3.1 APP 2.2 SH2D3C 2.3 FAS 1.7 FLI30092 3.0 LAMC1 2.2 RBP7 2.3 XK 1.7 BTN2A1 3.0 REPS2 2.2 ERGIC1 2.3 CARD16 1.7 HIF1A 3.0 PIK3CB 2.2 TMEM45B 2.3 SLAMF8 1.7 LOC440313 3.0 SMPDL3A 2.2 CUX1 2.2 SELP 1.6 HOXA1 3.0 UBE2C 2.2 TREM1 2.2 NDN 1.6 LOC645153 3.0 NDUFAF3 2.1 C1GALT1C1 2.2 OAS2 1.6 ST3GAL6 3.0 CDC20 2.1 MAML3 2.2 TAPBP 1.6 LONRF1 2.9 CT5K 2.1 C15orf29 2.2 BPI 1.6 PPP1R3B 2.9 RAB13 2.1 DSC2 2.2 DHX58 1.6 MPPE1 2.9 LOC651524 2.1 RRP12 2.1 GA56 1.6 LOC652699 2.9 TMEM176A 2.1 LRP3 2.1 CPT1B 1.6 LOC646144 2.8 PDGFC 2.1 HDAC7A 2.1 CD300C 1.6 SGM51 2.8 ATP9A 2.1 FOS 2.1 LILRA6 1.6 BMP2K 2.7 SV2A 2.0 C14orf4 2.1 USF1 1.6 SLC31A1 2.7 SPOSC1 2.0 LIPN 2.1 C2 1.6 ARSB 2.7 MARCO 2.0 MAPILC382 2.1 382310 1.6 CAMKID 2.6 CDC109A 2.0 LOC400793 2.1 NFXL1 1.6 ICAM4 2.6 NUSAP1 2.0 LOC647834 2.1 GCH2 1.6 HIF1A 2.6 SLCO4C1 2.0 PHF20L1 2.1 CCR1 1.6 LOC641996 2.6 CYP27A1 2.0 CCNJL 2.1 OAS2 1.6 RNASE10 2.5 LOC644615 2.0 SLC1ZA6 2.0 CCR2 1.6 PI15 2.5 PKM2 2.0 FL142957 2.0 F2RL1 1.6 SLC30A1 2.5 BMX 2.0 CCDC147 2.0 SNX20 1.6 LOC389124 2.5 PAD14 1.9 SLC25A40 2.0 ARAP2 1.6 ATP1A3 2.5 NAMPT 1.9 LOC649270

Table 8 (below). Top 50 overexpressed genes in the inflammation modules in the good-treatment response sarcoidosis patients

Fold change (good response vs no response/ inadequate response) Symbol 8.3 IL1R2 6.2 GRB10 5.4 CEACAM4 5.1 SIPA1L2 4.5 BMX 4.3 IL1RAP 4.0 REP52 4.0 ANXA3 4.0 MMP9 4.0 PHC2 3.8 HAUS4 3.6 DUSP2 3.6 CA4 3.4 SAMSN1 3.4 KLHL2 3.3 ACSL1 3.2 NSUN7 3.2 IL18RAP 3.2 GNG10 3.1 5MAP2 3.1 MGAM 3.1 LIN7A 3.1 IRAK3 3.0 USP10 3.0 CEBPD 3.0 TGFA 3.0 FOS 3.0 MANSC1 2.9 SLC26A8 2.8 ROPN1L 2.8 GPR97 2.8 NAMPT 2.8 MRVI1 2.8 KCNI15 2.7 KLHL8 2.7 GNG1D 2.7 MEGF9 2.7 GPR160 2.7 B4GAL7S 2.7 STEAP4 2.7 LRG1 2.7 FS 2.6 PHTF1 2.6 HMGB2 2.6 DGATZ 2.6 SLC11A1 2.6 QPCT 2.6 PANX2 2.6 GPR141 2.6 LMNB1

TABLE 9 Interferon inducible genes from Berry, et al. (5). Symbol CD274 CXCL10 GBP1 GBP2 GBP5 IFI16 IFI35 IFI44 IFI44L IFI6 IFIH1 IFIT2 IFIT3 IFIT5 IFITM1 IFITM3 IRF7 OAS1 OAS2 OAS3 SOCS1 STAT1 STAT2 TAP1 TAP2

FIG. 10B is a Venn diagram comparing the genes that distinguish between Tb, sarcoidosis, pneumonia and lung cancer, versus, Tb, active sarcoidosis, non-active sarcoidosis, pneumonia and lung cancer. The overlapping 1359 genes are included in the attached electronic table.

TABLE 10 List of Genes Downregulated in Tb versus Active Sarcoid Fold change Symbol TB vs Active Sarcoid Regulation MEF2D −1.6 DOWN BHLHB2 −1.7 DOWN CLC −2.3 DOWN FCER1A −2.5 DOWN SRGAP3 −2.6 DOWN FLJ43093 −2.8 DOWN CCR3 −2.9 DOWN EMR4 −3 DOWN ZNF792 −3.1 DOWN C10orf33 −3.5 DOWN CACNG6 −3.8 DOWN P2RY10 −4.2 DOWN GATA2 −4.6 DOWN EMR4P −6.6 DOWN ESPN −7 DOWN EMR4 −9.3 DOWN MXD4 −1.5 DOWN ZSCAN18 −1.6 DOWN

TABLE 11 List of 87 genes of FIG. 10B. ProbeID Probe_Sequence Symbol 3460168 GCTGCTTTTAGGTTAACCACAAAGGAACAACTCAGGATCAGTCGTGATTG PHF20L1 6180497 TACTGAAAGACTTTTGCCTAAAGTGGCATTATTGACTGCTGGTGTGATGA LOC400304 6400148 GAATACTTCTCTTGCTGAGAGCCGATGCCCGTCCCCGGGCCAGCAGGGAT SELM 1850041 TCAGACTCCCTGCCACCTTTTCCCCTGGGTTCTGCCGTCTTGCCTCACTT DPM2 2690561 CATGGGCTTTGGTCTTTTTGACTAAACCTCTTTTATAACATGTTCAATAA RPLP1 1400747 GCGGAAGAGGAGCCGCTGGAACCAAGACACAATGGAACAGAAGACAGTGA SF1 7650451 AGTGTCCTCGACATCCCAGGGGAAAGCAAGAGCAGTGAGCCTGAGCAGTG ZNF683 3850632 GAGCCGCCAGGAACCCTCCTCCTGTCAATGGGGGTGTAGTATTTTTGCCA CTTN 4880600 CCCCTTGAGAATGGTGATCCACCCAGTTACAGGGGCATTTAGGGAGCAGA PTCRA 1780008 GCAAGAAAGTCTAACCTATTCCGGTGTTCTCTCTCCCATGAGACAAGCCG SNORA28 7400475 TGTTAGCCCTGAAGATCTGGCTACCCCAATAGGAAGGCTGAAGGTTTCCC RPGRIP1 7510367 TGCCCCCTGACTGATAGCATTTCAGAATGTGTCTTTTGAAGGGCTATACC GPR160 1850035 CAGAGGCAGGAAAAGCAAGGAGCCAGAATTAAGAGGTTGGGTCAGTCTGC PPIA 4040546 AGGACGTGATCCTGCTTGGGGACTTCAATGCTGACTGCGCTTCACTGACC DNASE1L1 6100424 GCTGATCTGGCAGGATGCTCTCTTCAAGCATATCCAAAACCAGATGTGCC HEMGN 4390487 GAGCAGGGGAGAAATAGCAGAGGGGCTTGGAGGGTCACATAGGTAGATGG RAB13 2320047 ACATGGCCCGCAAGGACAATGAATCCACTCACATTGCAGAACAATTCCGA NFIA 2600187 GTGAGCCCAAAGTTCTGAAAGGTGTTGCGGCTCCTTCGCCTTCGTCAAAT LOC728843 5090630 CCCTGCCCTCATGTTGCTTTGGGTCTAGTGGAGGAGAGAGACAGATAAGC 7610750 CTCCTGCCACCCAGTGGCCTCTTTAGGCCAAGCTCATGCCTCACAAGGGC LOC100134660 3780767 GAGCAGCTCCCTCGCTGCGATCTATTGAAAGTCAGATCTCCACACAAGGG LOC100132564  580484 TGCAGCCGTCCATAGCAGTACCCCTAAAATCCCACCAGAATACGGGTCCC HIP1 3460669 AGATGTGGCCATTAAGGAGCCCCTAGTGGATGTCGTGGACCCCAAACAGC PRMT1 4850327 GATCCAGCCATTACTAACCTATTCCTTTTTTGGGGAAATCTGAGCCTAGC PDGFC 2350156 GCATCAGCGAGTGCGACAGTGTTGGCGTGGATGTTCTTTCGATGGTACTG NCRNA00085 3140386 CTTGCTTCAGTTGAGACCTACGTTTTGGCCAGTCCCAGCAGGAAGATATC NFATC3 3420687 AGGACACAGAGGAAAGGCTGAAACAACGGGAAGAGGTTTTGAGGAAAATC GIMAP7 6370110 ACTGCTCTTTAAGAGGGGACAAGAAATTGGGGGGACCCGAGGCCTTCCAG LOC100130905 4780619 GGGATTGGTACTTTTGGAAATCAGGTTGGATTTGTGAAGCTGGCAGAAGG AKAP7 6840047 TCAACGCCTGGAGGACGCCTTATGGAGCCAGCATATCCCAGTCTAAAGAA TLE3 1940368 ACACCCTACTGTCCTTGTGCCTCACGCCCCCTCCTCATCCTGCACCCCTT NRSN2 4280743 CCTGCGTCACAGGGAAGCAACCTACAGAGAAGCAGCAGCTCCCCAAGAGA RPL37  110372 GCCTCCTTGTTCCCTGTGGCTGCTGATAACCCAACATTCCATCTCTACCC CSTA 5080544 AACTAGCGAACCCCAGGGGAAGGTGCCGTGTGGAGAGCACTTTCGGATTC C20orf107  670189 TTGTCATGCTCCCCACAGAGAGCCCAGGACATTTGCCTGATGTATGGTGC TMEM169 7560164 CCGGGTACAGATCTCAGCAGTGCATAGCGAGGAAGACATTGACCGCTGCG GCAT 5720682 AGCAGCACTTGCCCATTCCTTACACCCCTTCCCCATCCTGCTCCGCTTCA TMEM176A   50136 TTTGCCTATGATGCCTTCAAGATCTACCGGACTGAGATGGCACCCGGGGC CMTM5 2030180 CAAGTTCTTAACCATCCCGGGTTCCAGTGGTTACAGAGTTCTGCCCTGGG 3610372 GGAAATGGGAGTGCTCAGTCTGTGCAAGTCAGAATCCTTGAAACTGGGCC C3orf26 2690240 ACTTGTGGACATCATGGATTGTCTAACACCATCACAGTCCCTGGCTCAGG FANCD2  770692 CTGCCTGGCTCCTCCTTGAGGCTGGAACTCTCTCCAGGGTGGTTAACTCT C9orf114 7050612 CAGAGGAACTTTGCTCAAGGCGCAGATCCGTCACCAGTCCCTTGACAGTC TIAM2 1110450 TGGGACACAGCTGGCCAAGAGCGGTTCAAGACAATAACTACTGCCTACTA LOC644615 4730746 CGCTGGGAGACCTTTGGGACGTGGGGTGGAATTTGGGGTATCTGTGCCTT PADI2 3800392 AGTGCTGCCCTCTGGGGACATGCGGAGTGGGGGTCTTATCCCTGTGCTGA GRINA 4050768 CAGAGCCCCTGGTGCAATGCGGTCACAGGTTTTATGGGACTTTGGTGAGC CHST13 3120326 ATTCTTGGTGGCTTCTTCATAGCAGGTAAGCCTCTCCTTCTAAAAACTTC ANGPT1 1260215 GTCAGATGCTGTTGGGTCACATAGAAGAACAAGATAAGGTCCTCCACTGC KIF27 1850364 GCCCTTCCTCTCCCATAAGATGGACAAAAGTGTTTCTGTATCACTGTGTC ZNF550 5270379 GCTAATCTTCAGCCCGTACCAAAAAGTAGAGTGGAGCCTCTTTGCACTAC PIK3C2A 3710450 GGAACAGACTGAGAAGGGCAAACATTCCTGGGAGCTGGGCAAGGAGATCC NR1H3 2970296 CAATTCTCCCAATGAGCCTTTTGTCTGTGGGAAAAGCAGGAGACGCTTCG ALG8 7560541 CTCCACTTTGCTGGTTCAGCCTTCGTGTGGCTCCTGGTAACGTGGCTCCA SLC2A5 2490411 GACTGTCAGGAAGGGTCGGAGTCTGTAAAACCAGCATACAGTTTGGCTTT ITGB5  780021 CACCTTCCTGGTCTGTTGGATGCCTTATATCGTGATCTGCTTCTTGGTGG OPN3 4880376 GGCTCTCCTAGTGCCCAGAGACAGGCCCAGAGGTTTACAAGTTTTCTAAG UBE2O 5670301 AAAGAAGGGCCCGAGCTTAGTTTCCCCAGGACTGGCCTAGGAAGGAGCAC RIN3 7320678 TGCATGAGATCACACAACTAGGCGGTGACTGAGTCCAACACACCAAAGCC 2900615 CATCAACAGCTAAACTGCACAGGGAGGAGGATCGAACGGATCCCTCCCGC LOC100129203 3400215 CTTCAAGGGTTCTGGAGGAGGGAAGGGTCTGCAGGTTCCATGGGTGACAG B3GNT1 1090286 CCAGGAGGATCCCTTGATCCCTTGTGGCCAGGAGTTGGGAGACCAGCCTG NEK8 4860181 ATGTGGAGGTGGCTGGTTCCCATGAACGTGGTTGTCAGAGGCGGGGGACA SLC38A5 5670437 CCCATCTCCAACTCGGAAGTAAGCCCAAGAGAACAACATAAAGCAAACAA GPR183 5260379 TCTCCAGGGGCAAACCTCTGATGTCTTCTTTGCAGCCAGTAGCTTGACTG LOC728748 2060280 GTACGACGTTTGATCCATGCCCATCCAAAAGGATGATGAAGTTCAGGTTG LOC646966 2030360 GTGAACACAGGCATGGCGGCAGAAGTGCCAAAAGTGAGCCCTCTCCAGCA FAM159A  450382 GCGGCTATCACCGAAGCAGGAGTGGCCAAAATGAAGTTTAATCCCTTTGT LOC441073 1770397 GAGCTGATTTGATCGAGGAGCGCGGTTACCGGACGGGCTGGGTCTATGGT CCNC 4010735 CCCTCCAAGGCAGCAAAGCAGAATCGGGAGCAGTGGAGCAGAAATGTGCA MRPL9 2140463 CTGTTCAGCTCAGGCACAGGGGCACAGCAGAGGTTTGGGAAGCGGTCTCC SLC37A1 5340458 ACGTGCTCCCTCTGCCAGGAGGAGAATGAAGACGTGGTGCGAGATGCGCT NSUN5 7320193 AAGAGGCCAAAGAGGCCCCAGCCGACAAGTGATCGCCCACAAGCCTTACT GHRL 4180768 TAGGATTCACACCCCACCTGCGCTTCACTTGGGTCCAGGCCTACTCCTGT ALAS2 3890228 GGCTAAATAGTCAAGGGGTAATATGGGCCTGTTGTTTAGTGTCTCCTTCC MPZL2 7330441 GAGTGGGCAGACATCGAAGCCAAACAGCAGTATCCCGGAAGCACTCATGC RNF13 4610538 GACTTTCCAGTTGGCCCTGATTTTCAACCATGTGATTGTTTCACTCCTGG SUMO1P1 2970612 GGGGAGGGTGGAAGAAATGGTGGACTGTATCTCTCACGTTCTGAAGCAGC UHRF2 1070079 GTGTCACTAAAGTTGGTATACAACCCCCCACTGCTAAATTTGACTGGCTT RNY4 3170241 TGGAAAAAGAGTTACCACGTGTTGCAGTGGTTCCTGACGCTGCTGCCCGC LOC651524 6370523 TCAATGTTCAGTGCTCAGGTATGTAGTAAGTACTGTAGTCCTGTGGGGGC KBTBD8 1580626 ACTCGTCTGACCCATCAGAGACGCCACAGCAGAGAAACACCTCTCAAATG ZNF224 2030403 ATGAACGTTCTCATTAACACGCAGGAGTACCGGGAGCCCTGAACCGCCCG OLIG1  650328 ATGCCATGCATACCTCCTGCCCCGCGGGACCACAATAAAAACCTTGGCAG TNFRSF4   10451 CCACAGCTTGGGGTGTTCAGCACTTGAGGACGGGTGGAGCTTGTTCAACC BEND7 7400593 GCACACGTTCTCGGGACCTCCTGAAGCTGCGTCACAGGCACTAATCAAAG LOC728323 2260538 GGCGGCAGAATGCCATCAAGTGTGGGTGGCTGAGGAAGCAAGGAGGCTTT ARHGAP24

TABLE 12 List of 37 genes of FIG. 10B. ProbeID Probe_Sequence Symbol 4250326 GGGAGGTCTGAGAGCCCTTAGCATGGGTGGTGTGCTGGGAGGTGGTGGGT LOC442132 2810139 GGTTATGCTGGGGGCGCGGTGGGCTCGCCTCAATACATTCACCACTCATA HOXA1   60674 TGGACCTGGAGGGTCTTCTGCTTGCTGGCTGTAGCTCCAGGTGCTCACTC LOC652102 2690634 AGCATACGGGACCAGGTCTACTATCCATGGCCAACTCTGGCCCAAACACC PPIE   50164 GATGGCACTGGACTCGCCGTTATCTTGAGGAGCCAGGAGCTGAAATGGCT C22orf27 6770044 TTGGGCCTGAGGAGCTGCCTGTTGTGGGCCAGCTGCTTCGACTGCTGCTT TEX10 1240270 GGATCTTCAGTTATTCGAGGGGAATGAGGCAGGTCAAGCCGATGCTAGCC LMTK2 7570184 GACCGTCGTGCCCCTCATCAAGGAAGAGCCAAGGACCCCAAGGAGAAGAA LOC283663 6560079 GCACTCAGGTCGTCATCAACTCCTTTTACATTGTGACACGGCCTTTGGCC SUCNR1 2030400 GGCCTGGGGAGATGTTGTTTTCATGCTGCTTCCACCATCACACTGGGGTT COLQ 3450338 CTCTCTTCCCTGATCCTTGGAGGAGCCCGAACTGATTCTGGAGCTCTGTG HLA-DOB 4390079 TGGGAAAGTGTGAGTTAATATTGGACACATTTTATCCTGATCCACAGTGG SAMSN1 3370255 CCGTTTGCTTCTTTAACTCCAGCCGCGGAATGACATTAGTGGAACCGGGC INPP5E 3990435 CCAGGAGGCCGAACACTTCTTTCTGCTTTCTTGACATCCGCTCACCAGGC 6840494 CAGCTCGGAGGAAGGTCTCCTATACACACAAAGCCTGGCATGCACCTTCG CYP4F3 3850010 CATTATTGGTTGGCTGCCAATGACCCCATATGTTCTGTGAGAATAGTAGC CRYZ 5810044 TTCTCTGGATGCCACGAGTACCAAGTTTTTAGAAGTAGAGCCATCCGTCT CDC14A 3440327 GCTGGGCTTGGCTGCCAAGAAGAAGGAGATAAACATCACCATCATCAAAG LOC653061 2900360 TCTATTAACACGGCACTTAGACACGTGCTGTTCCACCTTCCCTCGTGCTG KIR2DL4 4560435 CCTGGCAACCAGTGGGAAAAGAAACATGCGAGGCTGTAGGAAGAGGGAAG PCYOX1L 4780072 GCTTTAGATGTCAGTCTCGTTACCAGCAGCCTTTTGACCCAACTACGGCG TCEAL3 1030079 GTCCTGACTGCCTGGAGCATATTTGTGAATTCTCACTTGGAAGACTGGGG FRRS1 7150189 GCCTTTATGCCAGCCCGACACCTGCTGTAATTGGGGTGCATGAGCTATGG PHF17 3520168 TTCCAGGGCACGAGTTCGAGGCCAGCCTGGTCCACATGGGTCGGaaaaaa 3310504 CAGAAGTCCTAGACAGTGACATTTCTTAATGGTGGGAGTCCAGCTCATGC PDK4 2510561 CCTCCTCCCCTCTCCTGTACCAGAAAGAAGCCACAACTCATCACCGGAGA LOC440313 6110541 CCAGGACTAGCTTTTTGTGCCATGAGTTAGCCATGGTCCTGGACCCAGCA ZNF260 5290068 GAGCCCAGGGGTTAGAGACAAGCCTTGGCAACATAGCAAGATCCTGGCTC SLFN13  580465 TGATGGACCTCCCCGCTCCCTCAAGCTCTGGATGGCTGCAGTGTTGTACT VASH1 4280273 GGGTGGCAAGGACTGGAGTCAGTTGGAGAGTGCATAGCCAGTCTGTGAAG GM2A 5340646 CCTGCATCTGTATTTTATAGTCAGCCTTTTGACCACCTGGTGCCAGCTAT ASAP2 1500753 AGTGACTGTGGTGTCCTTGAGATGCTCACATTACTGCCCGGCCTGCCTCC VARS2 3930008 TAAGCCTTTGGATTTAAAGCCTGTTGAGGCTGGAGTTAGGAGGCAGATTG RPL14 7200025 ACTTCAATGTAGTTTTCCATCCTTCAAATAAACATGTCTGCCCCCATGGT KIR2DL1 5260717 CCGGCTTCTGGGTCTTTGAACAGCCGCGATGTCGATCTTCACCCCCACCA SBDSP 5570187 CAGCCTTCATCCATTAACTCTACTAGGGAGCCCACAGCCACCATTTCCAC S1PR3  650348 CTGCTTGCTAGGCTCAATTACCACTTCTGTTTGCTTTGTGGATCCTGGGA METTL1

Thus, in certain embodiments, the present invention includes the identification and/or differentiation of pulmonary diseases using the genes in the Tables of the present invention. Specifically, the skilled artisan will be able to differentiate the pulmonary diseases using 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 144, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1,000, 1,100, 1,200, 1,300, 1,400, or even 1,446 genes listed in the tables contained herein and filed herewith (genes, probes, and SEQ ID NOs incorporated herein by reference). The genes may be selected based on ease of use or accessibility, based on the genes that are most predictive (e.g., using the tables of the present invention), and/or based, in order of importance from top to bottom, of the lists provided for use in the analysis.

Study population and inclusion criteria. The majority of the TB patients were recruited from Royal Free Hospital, NHS Health Care Trust, London. The sarcoidosis patients were recruited from Royal Free Hospital, John Radcliffe Hospital in Oxford, St Mary's Hospital, Imperial College NHS Health Care Trust, and Barnet Hospital in London and the Avicenne Hospital in Paris. The pneumonia patients were recruited from Royal Free Hospital, NHS Health Care Trust, London. The lung cancer patients and 5 of the TB patients in the Test Set were recruited by the Lyon Collaborative Network, France. All patients were recruited consecutively over time such that the Training Set was recruited first followed by the Test Set, Validation Set and lastly the patients' samples that were used in the cell purification. Additional blood gene expression data were obtained from pulmonary and latent TB patients recruited and analysed in our earlier study, and additionally reanalysed in the current study, as presented in FIG. 6C (11).

The inclusion criteria were specific for each disease. Pulmonary TB patients: culture confirmed Mycobacterium tuberculosis in either sputum or bronchoalveolar lavage; pulmonary sarcoidosis: diagnosis made by a sarcoidosis specialist, granuloma's on biopsy, compatible clinical and radiological findings (within 6 months of recruitment) according to the WASOG guidelines (9); community acquired pneumonia patients: fulfilled the British Thoracic Society guidelines for diagnosis (10); lung cancer patients: diagnosis by a lung cancer specialist, histological and radiological features consistent with primary lung cancer; healthy controls: their gender, ethnicity and age were similar to the patients, negative QuantiFERON-TB Gold In-Tube (QFT) (Cellestis) test. The exclusion criteria for all patients and healthy controls included significant other medical history (including any immunosuppression such as HIV infection), aged below 18 years or pregnant. Patients were recruited between September 2009 and March 2012. Patients were recruited before commencing treatment unless otherwise stated. This study was approved by the Central London 3 Research Ethics Committee (09/H0716/4), and Ethical permission from CPP Sud-Est IV, France, CCPPRB, Pitié-salpétrierè Hospital, Paris. All participants gave written informed consent.

IFNγ release assay testing. The QFT M. tubercusosis antigen specific IFN-gamma release assay (IGRA) Assay (Cellestis) was performed according to the manufacturer's instructions.

Gene expression profiling. 3 ml of whole blood were collected into Tempus tubes (Applied Biosystems/Ambion) by standard phlebotomy, vigorously mixed immediately after collection, and stored between −20 and −80° C. before RNA extraction. RNA was isolated using 1.5 ml whole blood and the MagMAX-96 Blood RNA Isolation Kit (Applied Biosystems/Ambion) according to the manufacturer's instructions. 250 μg of isolated total RNA was globin reduced using the GLOBINclear 96-well format kit (Applied Biosystems/Ambion) according to the manufacturer's instructions. Total and globin-reduced RNA integrity was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies). RNA yield was assessed using a NanoDrop8000 spectrophotometer (NanoDrop Products, Thermo Fisher Scientific). Biotinylated, amplified antisense complementary RNA (cRNA) targets were then prepared from 200-250 ng of the globin-reduced RNA using the Illumina CustomPrep RNA amplification kit (Applied Biosystems/Ambion). 750 ng of labelled cRNA was hybridized overnight to Illumina Human HT-12 V4 BeadChip arrays (Illumina), which contained more than 47,000 probes. The arrays were washed, blocked, stained and scanned on an Illumina iScan, as per manufacturer's instructions. GenomeStudio (Illumina) was then used to perform quality control and generate signal intensity values.

Cell purification and RNA processing for microarray. Whole blood was collected in sodium heparin. Peripheral blood mononuclear cells (PBMCs) were separated from the granulocytes/erythrocytes using a Lymphoprep™ (Axis-Shield) density gradient. Monocytes (CD14+), CD4+ T cells (CD4+) and CD8+T cells (CD8+) were isolated sequentially from the PBMCs using magnetic antibody-coupled (MACS) whole blood beads (Miltenyi Biotec, Germany) according to manufacturer's instructions. Neutrophils were isolated from the granulocyte/erythrocyte layer after red blood cell lysis using the CD15+MACS beads (Miltenyi Biotec, Germany). RNA was extracted from whole blood (5′ Prime PerfectPure Kit) or separated cell populations (Qiagen RNeasy Mini Kit). Total RNA integrity and yield was assessed as described above. Biotinylated, amplified antisense complementary RNA (cRNA) targets were then prepared from 50 ng of total RNA using the NuGEN WT-Ovation™ RNA Amplification and Encore BiotinIL Module (NuGEN Technologies, Inc). Amplifed RNA was purified using the Qiagen MinElute PCR purification kit (Qiagen, Germany). cRNA was then handled as described above.

Raw data processing. After microarray raw data were processed using GeneSpring GX version 11.5 (Agilent Technologies) and the following was applied to all analyses. After background subtraction each probe was attributed a flag to denote its signal intensity detection p-value. Flags were used to filter out probe sets that did not result in a ‘present’ call in at least 10% of the samples, where the ‘present’ lower cut off=0.99. Signal values were then set to a threshold level of 10, log 2 transformed, and per-chip normalised using 75th percentile shift algorithm. Next per-gene normalisation was applied by dividing each messenger RNA transcript by the median intensity of all the samples. All statistical analysis was performed after this stage. Raw microarray data has been deposited with GEO (Accession number GSE). All data collected and analysed in the experiments adhere to the Minimal Information About a Microarray Experiment (MIAME) guidelines.

Data analysis. GeneSpring 11.5 was used to select transcripts that displayed expression variability from the median of all transcripts (unsupervised analysis). A filter was set to include only transcripts that had at least twofold changes from the median and present in ≧10% of the samples. Unsupervised analysis was used to derive the 3422-transcripts. Applying a non-parametric statistical filter (Kruskal Wallis test with a FDR (Benjamini Hochberg)=0.01), after the unsupervised analysis, generated the 1446-transcript and 1396-transcript signatures. The two signatures differed only in which groups the statistical filter was applied across; 1446, five groups (TB, sarcoidosis, pneumonia, lung cancer and controls) and 1396, six groups (TB, active sarcoidosis, non-active sarcoidosis, pneumonia, lung cancer and controls).

Differentially expressed genes for each disease were derived by comparing each disease to a set of controls matched for ethnicity and gender within a 10% difference. GeneSpring 11.5 was used to select transcripts that were ≧1.5 fold different in expression from the mean of the controls and statistically significant (Mann Whitney unpaired FDR (Benjamini Hochberg)=0.01). Comparison Ingenuity Pathway Analysis (IPA) (Ingenuity Systems, Inc., Redwood, Calif.) was used to determine the most significant canonical pathways associated with the differentially expressed genes of each disease relative to the other diseases (Fisher's exact FDR (Benjamini Hochberg)=0.05). The bottom x-axis and bars of each comparison IPA graph indicated the log(p-value) and the top x-axis and line indicated the percentage of genes present in the pathway.

Molecular distance to health (MDTH) was determined as previously described (12), and then applied to different transcriptional signatures. Transcriptional modular analysis was applied as previously described (12). The raw expression levels of all transcripts significantly detected from background hybridisation were compared between each sample and all the controls present in that dataset. The percentage of significantly expressed genes in each module were represented by the colour intensity (Student t-test, p<0.05), red indicates overexpression and blue indicates underexpression. The mean percentage of significant genes and the mean fold change of these genes compared to the controls in specified modules were also shown in graphical form. MDTH and modular analysis were calculated in Microsoft Excel 2010. GraphPad Prism version 5 for Windows was used to generate the graphs.

Differentially expressed genes between the Training Set TB patients and active sarcoidosis patients were derived using the non-parametric Significance Analysis of Microarrays (q<0.05) and ≧1.5 fold expression change. Class prediction was performed within GeneSpring 11.5 using the machine learned algorithm support vector machines (SVM). The model was built using sample classifiers ‘TB’ or ‘not TB’. The SVM model should be built in one study cohort and run in an independent cohort to prevent over-fitting the predictive signature. This was possible for all the cohorts from our study. Where the study cohorts used a different microarray platform the SVM model had to be re-built in that cohort. To reduce the effects of over-fitting the same SVM parameters were always used. The kernel type used was linear, maximum iterations 100,000, cost 100, ratio 1 and validation type N-fold where N=3 with 10 repeats.

Univariate and multivariate regression analysis were calculated using STATA 9 (StataCorp 2005. Stata Statistical Software: Release 9. College Station, Tex.; StataCorp LP). In the multivariate regression analysis where there were missing data points (serum ACE and HRCT disease activity) to prevent list-wise deletion dummy variable adjustment was used.

It is contemplated that any embodiment discussed in this specification can be implemented with respect to any method, kit, reagent, or composition of the invention, and vice versa. Furthermore, compositions of the invention can be used to achieve methods of the invention.

It will be understood that particular embodiments described herein are shown by way of illustration and not as limitations of the invention. The principal features of this invention can be employed in various embodiments without departing from the scope of the invention. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, numerous equivalents to the specific procedures described herein. Such equivalents are considered to be within the scope of this invention and are covered by the claims.

All publications and patent applications mentioned in the specification are indicative of the level of skill of those skilled in the art to which this invention pertains. All publications and patent applications are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference.

The use of the word “a” or “an” when used in conjunction with the term “comprising” in the claims and/or the specification may mean “one,” but it is also consistent with the meaning of “one or more,” “at least one,” and “one or more than one.” The use of the term “or” in the claims is used to mean “and/or” unless explicitly indicated to refer to alternatives only or the alternatives are mutually exclusive, although the disclosure supports a definition that refers to only alternatives and “and/or.” Throughout this application, the term “about” is used to indicate that a value includes the inherent variation of error for the device, the method being employed to determine the value, or the variation that exists among the study subjects.

As used in this specification and claim(s), the words “comprising” (and any form of comprising, such as “comprise” and “comprises”), “having” (and any form of having, such as “have” and “has”), “including” (and any form of including, such as “includes” and “include”) or “containing” (and any form of containing, such as “contains” and “contain”) are inclusive or open-ended and do not exclude additional, unrecited elements or method steps.

The term “or combinations thereof” as used herein refers to all permutations and combinations of the listed items preceding the term. For example, “A, B, C, or combinations thereof” is intended to include at least one of: A, B, C, AB, AC, BC, or ABC, and if order is important in a particular context, also BA, CA, CB, CBA, BCA, ACB, BAC, or CAB. Continuing with this example, expressly included are combinations that contain repeats of one or more item or term, such as BB, AAA, AB, BBC, AAABCCCC, CBBAAA, CABABB, and so forth. The skilled artisan will understand that typically there is no limit on the number of items or terms in any combination, unless otherwise apparent from the context. In certain embodiments, the present invention may also include methods and compositions in which the transition phrase “consisting essentially of” or “consisting of” may also be used.

As used herein, words of approximation such as, without limitation, “about”, “substantial” or “substantially” refers to a condition that when so modified is understood to not necessarily be absolute or perfect but would be considered close enough to those of ordinary skill in the art to warrant designating the condition as being present. The extent to which the description may vary will depend on how great a change can be instituted and still have one of ordinary skilled in the art recognize the modified feature as still having the required characteristics and capabilities of the unmodified feature. In general, but subject to the preceding discussion, a numerical value herein that is modified by a word of approximation such as “about” may vary from the stated value by at least ±1, 2, 3, 4, 5, 6, 7, 10, 12 or 15%.

All of the compositions and/or methods disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the compositions and methods of this invention have been described in terms of preferred embodiments, it will be apparent to those of skill in the art that variations may be applied to the compositions and/or methods and in the steps or in the sequence of steps of the method described herein without departing from the concept, spirit and scope of the invention. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined by the appended claims.

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What is claimed is:
 1. A method of determining if a human subject is afflicted with pulmonary disease comprising: obtaining a sample from a subject suspected of having a pulmonary disease; determining the expression level of six or more genes from each of the following genes expressed in one or more of the following expression pathways: EIF2 signaling; mTOR signaling; regulation of eIF4 and p70s6K signaling; interferon signaling; antigen presentation pathways; T cell signaling pathways; and other signaling pathways; comparing the expression level of the six or more genes with the expression level of the same genes from individuals not afflicted with a pulmonary disease, and determining the level of expression of the six or more genes in the sample from the subject relative to the samples from individuals not afflicted with a pulmonary disease for the genes expressed in the one or more expression pathways, wherein co-expression of genes in the EIF2 signaling and mTOR signaling pathways are indicative of active sarcoidosis; co-expression of genes in the regulation of eIF4 and p70s6K signaling pathways is indicative of pneumonia; co-expression of genes in the interferon signaling and antigen presentation pathways are indicative of tuberculosis; and co-expression of genes in the T cell signaling pathways; and other signaling pathways is indicative of lung cancer.
 2. The method of claim 1, wherein the genes associated with tuberculosis are selected from at least 3, 4, 5 or 6 genes selected from ANKRD22; FCGR1A; SERPING1; BATF2; FCGR1C; FCGR1B; LOC728744; IFITM3; EPSTI1; GBP5; IF144L; GBP6; GBP1; LOC400759; IFIT3; AIM2; SEPT4; C1QB; GBP1; RSAD2; RTP4; CARD17; IFIT3; CASP5; CEACAM1; CARD17; ISG15; IF127; TIMM10; WARS; IF16; TNFAIP6; PSTPIP2; IF144; SCO2; FBXO6; FER1L3; CXCL10; DHRS9; OAS1; STAT1; HP; DHRS9; CEACAM1; SLC26A8; CACNA1E; OLFM4; and APOL6, wherein the genes are evaluated at least one of: in aggregate, in the order listed, aggregated into pathways, or selected from 7, 8, 9, 10, 11, 12, 13, 15, 20, 25, 35, 40, 45, or 49 genes.
 3. The method of claim 1, wherein the genes associated with tuberculosis and not active sarcoidosis, pneumonia or lung cancer are selected from C1QB; IF127; SMARCD3; SOCS1; KCNJ15; LPCAT2; ZDHHC19; FYB; SP140; IFITM1; ALAS2; CEACAM6; OAS2; C1QC; LOC100133565; ITGA2B; LY6E; SP140; CASP7; GADD45G; FRMD3; CMPK2; AQP10; CXCL14; ITPRIPL2; FAS; XK; CARD16; SLAMF8; SELP; NDN; OAS2; TAPBP; BPI; DHX58; GAS6; CPT1B; CD300C; LILRA6; USF1; C2; 38231.0; NFXL1; GCH1; CCR1; OAS2; CCR2; F2RL1; SNX20; and ARAP2, wherein the genes are evaluated at least one of: in aggregate, in the order listed, aggregated into pathways, or selected from 7, 8, 9, 10, 11, 12, 13, 15, 20, 25, 35, 40, 45, or 49 genes.
 4. The method of claim 1, wherein the genes associated with active sarcoidosis are selected from FCGR1A; ANKRD22; FCGR1C; FCGR1B; SERPING1; FCGR1B; BATF2; GBP5; GBP1; IFIT3; ANKRD22; LOC728744; GBP1; EPSTI1; IF144L; INDO; IFITM3; GBP6; RSAD2; DHRS9; TNFAIP6; IFIT3; P2RY14; DHRS9; ID01; STAT1; WARS; TIMM10; P2RY14; LOC389386; FER1L3; IFIT3; RTP4; SCO2; GBP4; IFIT1; LAP3; OASL; CEACAM1; LIMK2; CASP5; STAT1; CCL23; WARS; ATF3; IF16; PSTPIP2; ASPRV1; FBXO6; and CXCL10, wherein the genes are evaluated at least one of: in aggregate, in the order listed, aggregated into pathways, or selected from 7, 8, 9, 10, 11, 12, 13, 15, 20, 25, 35, 40, 45, or 49 genes.
 5. The method of claim 1, wherein the genes associated with active sarcoidosis and not tuberculosis, pneumonia or lung cancer are selected from CCL23; PIK3R6; EMR4; CCDC146; KLF4; GRINA; SLC4A1; PLA2G7; GRAMD1B; RAPGEF1; NXNL1; TRIM58; GABBR1; TAGLN; KLF4; MFAP3L; LOC641798; RIPK2; LOC650840; FLJ43093; ASAP2; C15orf26; REC8; KIAA0319L; GRINA; FLJ30092; BTN2A1; HIF1A; LOC440313; HOXA1; LOC645153; ST3GAL6; LONRF1; PPP1R3B; MPPE1; LOC652699; LOC646144; SGMS1; BMP2K; SLC31A1; ARSB; CAMK1D; ICAM4; HIF1A; LOC641996; RNASE10; PI15; SLC30A1; LOC389124; and ATP1A3, wherein the genes are evaluated at least one of: in aggregate, in the order listed, aggregated into pathways, or selected from 7, 8, 9, 10, 11, 12, 13, 15, 20, 25, 35, 40, 45, or 49 genes.
 6. The method of claim 1, wherein the genes associated with pneumonia are selected from OLFM4; LTF; VNN1; HP; DEFA4; OPLAH; CEACAM8; DEFA1B; ELANE; C19orf59; ARG1; CDK5RAP2; DEFA1B; DEFA3; DEFA1B; FCGR1A; MMP8; FCGR1B; SLPI; SLC26A8; MAPK14; CAMP; NLRC4; FCAR; RNASE3; FCGR1B; NAIP; OLR1; FCGR1C; ANXA3; DEFA1; PGLYRP1; TCN1; ANKDD1A; COL17A1; SLC26A8; TMEM144; SAMD14; MAPK14; RETN; NAIP; GPR84; CASP5; MPO; MMP9; CR1; MYL9; CLEC4D; ITGAX; and ANKRD22, wherein the genes are evaluated at least one of: in aggregate, in the order listed, aggregated into pathways, or selected from 7, 8, 9, 10, 11, 12, 13, 15, 20, 25, 35, 40, 45, or 49 genes.
 7. The method of claim 1, wherein the genes associated with pneumonia and not tuberculosis, active sarcoidosis, or lung cancer are selected from DEFA4; ELANE; MMP8; OLR1; COL17A1; RETN; GPR84; LOC100134379; TACSTD2; SLC2A11; LOC100130904; MCTP2; AZU1; DACH1; GADD45A; NSUN7; CR1; CDK5RAP2; LOC284648; GPR177; CLEC5A; UPB1; SLC2A5; GPR177; APP; LAMC1; REPS2; PIK3CB; SMPDL3A; UBE2C; NDUFAF3; CDC20; CTSK; RAB13; LOC651524; TMEM176A; PDGFC; ATP9A; SV2A; SPOCD1; MARCO; CCDC109A; NUSAP1; SLCO4C1; CYP27A1; LOC644615; PKM2; BMX; PADI4; and NAMPT, wherein the genes are evaluated at least one of: in aggregate, in the order listed, aggregated into pathways, or selected from 7, 8, 9, 10, 11, 12, 13, 15, 20, 25, 35, 40, 45, or 49 genes.
 8. The method of claim 1, wherein the genes associated with lung cancer are selected from ARG1; TPST1; FCGR1A; C19orf59; SLPI; FCGR1B; IL1R1; FCGR1C; TDRD9; SLC26A8; FCGR1B; CLEC4D; LOC100132858; SLC22A4; LOC100133177; SIPA1L2; ANXA3; LIMK2; TMEM88; MMP9; ASPRV1; MANSC1; TLR5; CD163; CAMP; LOC642816; DPRXP4; LOC643313; NTN3; MRVI1; F5; SOCS3; TncRNA; MIR21; LOC100170939; LOC100129904; GRB10; ASGR2; LOC642780; LOC400499; FCAR; KREMEN1; SLC22A4; CR1; LOC730234; SLC26A8; C7orf53; VNN1; NLRC4; and LOC400499, wherein the genes are evaluated at least one of: in aggregate, in the order listed, aggregated into pathways, or selected from 7, 8, 9, 10, 11, 12, 13, 15, 20, 25, 35, 40, 45, or 49 genes.
 9. The method of claim 1, wherein the genes associated with lung cancer and not tuberculosis, active sarcoidosis, or pneumonia are selected from TPST1; MRVI1; C7orf53; ECHDC3; LOC651612; LOC100134660; TIAM2; KIAA1026; HECW2; TLE3; TBC1D24; LOC441193; CD163; RFX2; LOC100134688; LOC642342; FKBP9L; PHF20L1; LOC402176; CD163; OSBPL1A; PRMT5; UBTD1; ADORA3; SH2D3C; RBP7; ERGIC1; TMEM45B; CUX1; TREM1; C1GALT1C1; MAML3; C15orf29; DSC2; RRP12; LRP3; HDAC7A; FOS; C14orf4; LIPN; MAP1LC3B2; LOC400793; LOC647834; PHF20L1; CCNJL; SLC12A6; FLJ42957; CCDC147; SLC25A40; and LOC649270, wherein the genes are evaluated at least one of: in aggregate, in the order listed, aggregated into pathways, or selected from 7, 8, 9, 10, 11, 12, 13, 15, 20, 25, 35, 40, 45, or 49 genes.
 10. The method of claim 1, wherein the genes associated with lung cancer and not tuberculosis, active sarcoidosis, or pneumonia are selected from Table 1 by: parsing the genes into the expression pathways, and determining that the subject is afflicted with a pulmonary disease selected from tuberculosis, sarcoidosis, cancer or pneumonia based on the gene expression from a sample obtained from the subject when compared to the level of expression of the genes in each of the expression pathways.
 11. The method of claim 1, wherein the specificity is 90 percent or greater and sensitivity is 80 percent or greater for a diagnosis of tuberculosis or sarcoidosis.
 12. The method of claim 1, further comprising a method for displaying if the patient has tuberculosis or sarcoidosis aggregating the expression data from the 3, 4, 5, 6 or more genes into a single visual display of a vector of expression for tuberculosis, sarcoidosis, cancer or an infectious pulmonary disease.
 13. The method of claim 1, further comprising the step of detecting and evaluating 7, 8, 9, 10, 12, 15, 20, 25, 35, 50, 75, 90, 100, 125, or 144 genes for the analysis.
 14. The method of claim 1, further comprising the step of detecting and evaluating the EIF2 signaling; mTOR signaling; regulation of eIF4 and p70s6K signaling; interferon signaling; antigen presentation pathways; T cell signaling pathways; and other signaling pathways from 7, 8, 9, 10, 12, 15, 20, 25, 35, 50, 75, 90, 100, 125, or 144 genes that are upregulated or downregulated and are selected from UBE2J2; ALPL; JMJD6; FCER1G; LILRA5; LY96; FCGR1C; C10orf33; GPR109B; PROK2; PIM3; SH3GLB1; DUSP3; PPAP2C; SLPI; MCTP1; KIF1B; FLJ32255; BAGE5; IFITM1; GPR109A; IF135; LOC653591; KREMEN1; IL18R1; CACNA1E; ABCA2; CEACAM1; MXD4; TncRNA; LMNB1; H2AFJ; HP; ZNF438; FCER1A; SLC22A4; DISC1; MEFV; ABCA1; ITPRIPL2; KCNJ15; LOC728519; ERLIN1; NLRC4; B4GALT5; LOC653610; HIST2H2BE; AIM2; P2RY10; CCR3; EMR4P; NTN3; C1QB; TAOK1; FCGR1B; GATA2; FKBP5; DGAT2; TLR5; CARD17; INCA; MSL3L1; ESPN; LOC645159; C19orf59; CDK5RAP2; PLSCR1; RGL4; IFI30; LOC641710; GAGGCTTTCAGGTAGGAGGACAATGGTAGCACTGTAGGTCCCCAGTGTCG (SEQ ID NO.: 754); LOC100008589; LOC100008589; SMARCD3; NGFRAP1; LOC100132394; OPLAH; CACNG6; LILRB4; HIST2H2AA4; CYP1B1; PGS1; SPATA13; PFKFB3; HIST1H3D; SNORA73B; SLC26A8; SULT1B1; ADM; HIST2H2AA3; HIST2H2AA3; GYG1; CST7; EMR4; LILRA6; MEF2D; IFITM3; MSL3; DHRS13; EMR4; C16orf57; HIST2H2AC; EEF1D; TDRD9; GPR97; ZNF792; LOC100134364; SRGAP3; FCGR1A; HPSE; LOC728417; LOC728417; MIR21; HIST1H2BG; COP1; SMARCD3; LOC441763; ZSCAN18; GNG8; MTRF1L; ANKRD33; PLAC8; PLAC8; SLC26A8; AGTRAP; FLJ43093; LPCAT2; AGTRAP; S100A12; SVIL; LILRA5; LILRA5; ZFP91; CLC; LOC100133565; LTB4R; SEPT04; ANXA3; BHLHB2; IL4R; IFNAR1; MAZ; GCCCCCTAATTGACTGAATGGAACCCCTCTTGACCAAAGTGACCCCAGAA (SEQ ID NO.: 1379); OSM; and optionally excluding at least one of ADM, SEPT4, IFITM1, FCER1G, MED2F, CDK5RAP2 or CARD16.
 15. The method of claim 14, wherein the genes that are downregulated are selected from MEF2D; BHLHB2; CLC; FCER1A; SRGAP3; FLJ43093; CCR3; EMR4; ZNF792; C10orf33; CACNG6; P2RY10; GATA2; EMR4P; ESPN; EMR4; MXD4; and ZSCAN18.
 16. The method of claim 14, wherein the interferon inducible genes are selected from CD274; CXCL10; GBP1; GBP2; GBP5; IF116; IF135; IF144; IF144L; IF16; IFIH1; IFIT2; IFIT3; IFIT5; IFITM1; IFITM3; IRF7; OAS1; OAS2; OAS3; SOCS1; STAT1; STAT2; TAP1; and TAP2.
 17. The method of claim 1, wherein the sample is a blood, peripheral blood mononuclear cells, sputum, or lung biopsy.
 18. The method of claim 1, wherein the expression level comprises a mRNA expression level and is quantitated by a method selected from the group consisting of polymerase chain reaction, real time polymerase chain reaction, reverse transcriptase polymerase chain reaction, hybridization, probe hybridization and gene expression array.
 19. The method of claim 1, wherein the expression level is determined using at least one technique selected from the group consisting of polymerase chain reaction, heteroduplex analysis, single stand conformational polymorphism analysis, ligase chain reaction, comparative genome hybridization, Southern blotting, Northern blotting, Western blotting, enzyme-linked immunosorbent assay, fluorescent resonance energy-transfer and sequencing.
 20. The method of claim 1, wherein the expression level is determined by microarray analysis that comprises use of oligonucleotides that hybridize to mRNA transcripts or cDNAs for the six or more genes, and wherein the oligonucleotides are disposed or directly synthesized on the surface of a chip or wafer.
 21. The method of claim 20, wherein the oligonucleotides are about 10 to about 50 nucleotides in length.
 22. The method of claim 1, further comprising the step of using the determined comparative gene product information to formulate at least one of diagnosis, a prognosis or a treatment plan.
 23. The method of claim 1, wherein the patient's disease state is further determined by radiological analysis of the patient's lungs.
 24. The method of claim 1, further comprising the step of determining a treated patient gene expression dataset after the patient has been treated and determining if the treated patient gene expression dataset has returned to a normal gene expression dataset thereby determining if the patient has been treated.
 25. A method of determining a lung disease from a patient suspected of sarcoidosis, tuberculosis, lung cancer or pneumonia comprising: obtaining a sample from the patient suspected of sarcoidosis, tuberculosis, lung cancer or pneumonia; detecting expression of 3, 4, 5, 6 or more disease genes, markers, or probes of Table 1 (SEQ ID NOS.: 1 to 1446), wherein increased expression of mRNA of upregulated sarcoidosis, tuberculosis, lung cancer and pneumonia markers of Table 1 and/or decreased expression of mRNA of downregulated sarcoidosis, tuberculosis, lung cancer or pneumonia markers of Table 1 relative to the expression of the mRNAs from a normal sample; and determining the lung disease based on the expression level of the six or more disease markers of Table 1 based on a comparison of the expression level of sarcoidosis, tuberculosis, lung cancer, and pneumonia.
 26. The method of claim 25, further comprising the step of selecting 3, 4, 5, 6 or more genes that are differentially expressed between sarcoidosis, tuberculosis, lung cancer, and pneumonia.
 27. The method of claim 25, further comprising the step of differentiating between sarcoidosis that is active sarcoidosis and inactive sarcoidosis by determining the expression levels of six or more genes, markers, or probes selected from: TMEM144; FBLN5; FBLN5; ERI1; CXCR3; GLUL; LOC728728; KLHDC8B; KCNJ15; RNF125; CCNB1IP1; PSG9; LOC100170939; QPCT; CD177; LOC400499; LOC400499; LOC100134634; TMEM88; LOC729028; EPSTI1; INSC; LOC728484; ERP27; CCDC109A; LOC729580; C2; TTRAP; ALPL; MAEA; COX10; GPR84; TRMT11; ANKRD22; MATK; TBC1D24; LILRA5; TMEM176B; CAMP; PKIA; PFTK1; TPM2; TPM2; PRKCQ; PSTPIP2; LOC129607; APRT; VAMPS; FCGR1C; SHKBP1; CD79B; SIGIRR; FKBP9L; LOC729660; WDR74; LOC646434; LOC647834; RECK; MGST1; PIWIL4; LILRB1; FCGR1B; NOC3L; ZNF83; FCGBP; SNORD13; LOC642267; GBP5; EOMES; C5; CHMP7; ETV7; ILVBL; LOC728262; GNLY; LOC388572; GATA1; MYBL1; LOC441124; IL12RB1; BRIX1; GAS6; GAS6; LOC100133740; GPSM1; C6orf129; IER3; MAPK14; PROK1; GPR109B; SASP; LOC728093; PROK2; CTSW; ABHD2; LOC100130775; SLITRK4; FBXW2; RTTN; TAF15; FUT7; DUSP3; LOC399715; LOC642161; TCTN1; SLAMF8; TGM2; ECE1; CD38; INPP4B; ID3; CR1; CR1; TAPBP; PPAP2C; MBOAT2; MS4A2; FAM176B; LOC390183; SERPING1; LOC441743; H1F0; SOD2; LOC642828; POLB; TSPAN9; ORMDL3; FER1L3; LBH; PNKD; SLPI; SIRPB1; LOC389386; REC8; GNLY; GNLY; FOLR3; LOC730286; SKAP1; SELP; DHX30; KIAA1618; NQO2; ANKRD46; LOC646301; LOC400464; LOC100134703; C20orf106; SLC25A38; YPEL1; IL1R1; EPHA1; CHD6; LIMK2; LOC643733; LOC441550; MGC3020; ANKRD9; NOD2; MCTP1; BANK1; ZNF30; FBXO7; FBXO7; ABLIM1; LAMP3; CEBPE; LOC646909; BCL11B; TRIM58; SAMD3; SAMD3; MYOF; TTPAL; LOC642934; FLJ32255; LOC642073; CAMKK2; OAS2; RASGRP1; CAPG; LOC648343; CETP; CETP; CXCR7; UBASH3A; LOC284648; IL1R2; AGK; GTPBP8; LEF1; LEF1; GPR109A; IF135; IRF7; IRF7; SP4; IL2RB; ABLIM1; TAPBP; MAL; TCEA3; KREMEN1; KREMEN1; VNN1; GBP1; GBP1; UBE2C; DET1; ANKRD36; DEFA4; GCH1; IL7R; TMCO3; FBXO6; LACTB; LOC730953; LOC285296; IL18R1; PRR5; LOC400061; TSEN2; MGC15763; SH3YL1; ZNF337; AFF3; TYMS; ZCCHC14; SLC6A12; LY6E; KLF12; LOC100132317; TYW3; BTLA; SLC24A4; NCALD; ORAI2; ITGB3BP; GYPE; DOCKS; RASGRP4; LOC339290; PRF1; TGFBR3; LGALS9; LGALS9; BATF2; MGC57346; TXK; DHX58; EPB41L3; LOC100132499; LOC100129674; GDPD5; ACP2; C3AR1; APOB48R; UTRN; SLC2A14; CLEC4D; PKM2; CDCA5; CACNA1E; OSBPL3; SLC22A15; VPREB3; LOC642780; MEGF6; LOC93622; PFAS; LOC729389; CREBZF; IMPDH1; DHRS3; AXIN2; DDX60L; TMTC1; ABCA2; CEACAM1; CEACAM1; FLJ42957; SIAH2; DDAH2; C13orf18; TAGLN; LCN2; RELB; NR1I2; BEND7; PIK3C2B; IF16; DUT; SETD6; LOC100131572; TNRC6A; LOC399744; MAPK13; TAP2; CCDC15; TncRNA; SIPA1L2; HIST1H4E; PTPRE; ELANE; TGM2; ARSD; LOC651451; CYFIP1; CYFIP1; LOC642255; ASCC2; ZNF827; STAB1; LMNB1; MAP4K1; PSMB9; ATF3; CPEB4; ATP5S; CD5; SYTL2; H2AFJ; HP; SORT1; KLHL18; HIST1H2BK; KRTAP19-6; RNASE2; LOC100134393; C11orf82; BLK; CD160; LOC100128460; CD19; ZNF438; MBNL3; MBNL3; LOC729010; NAGA; FCER1A; C6orf25; SLC22A4; LOC729686; CTSL1; BCL11A; ACTA2; KIAA1632; UBE2C; CASP4; SLC22A4; SFT2D2; TLR2; C10orf105; EIF2AK2; TATDN1; RAB24; FAH; DISC1; LOC641848; ARG1; LCK; WDFY3; RNF165; MLKL; LOC100132673; ANKDD1A; MSRB3; LOC100134379; MEFV; C12orf57; CCDC102A; LOC731777; LOC729040; TBC1D8; KLRF1; KLRF1; ABCA1; LOC650761; LOC653867; LOC648710; SLC2A11; LOC652578; GPR114; MANSC1; MANSC1; DGKA; LIN7A; ITPRIPL2; ANO9; KCNJ15; KCNJ15; LOC389386; LOC100132960; LOC643332; SFI1; ABCE1; ABCE1; SERPINA1; OR2W3; ABI3; LOC400759; LOC728519; LOC654053; LOC649553; HSD17B8; C16orf30; GADD45G; TPST1; GNG7; SV2A; LOC649946; LOC100129697; RARRES3; C8orf83; TNFSF13B; SNRPD3; LOC645232; PI3; WDFY1; LOC100133678; BAMBI; POPS; TARBP1; IRAK3; ZNF7; NLRC4; SKAP1; GAS7; C12orf29; KLRD1; ABHD15; CCDC146; CASP5; AARS2; LOC642103; LOC730385; GAR1; MAF; ARAP2; C16orf7; HLA-C; FLJ22662; DACH1; CRY1; CRY1; LRRC25; KIAA0564; UPF3A; MARCO; SRPRB; MAD1L1; LOC653610; P4HTM; CCL4L1; LAPTM4B; MAPK14; CD96; TLR7; KCNMB1; P2RX7; LOC650140; LOC791120; LTF; C3orf75; GPX7; SPRYD5; EEF1B2; CTDSPL; HIST2H2BE; SLC38A1; AIM2; LOC100130904; LOC650546; P2RY10; ILSRA; MMP8; LOC100128485; RPS23; HDAC7; GUCY1A3; TGFA; NAIP; NAIP; NELL2; SIDT1; SLAMF1; MAPK14; CCR3; MKNK1; D4S234E; NBN; LOC654346; FGFBP2; BTLA; LRRN3; MT2A; LOC728790; LOC646672; NTN3; CD8A; CD8A; ZBP1; LDOC1L; CHM; LOC440731; LOC100131787; TNFRSF10C; LOC651612; STX11; LOC100128060; C1QB; PVRL2; ZMYND15; TRAPPC2P1; SECTM1; TRAT1; CAMKK2; CXCR5; CD163; FAS; RPL12P6; LOC100134734; CD36; FCGR1B; NR3C2; CSGALNACT2; GATA2; EBI2; EBI2; FKBP5; CRISPLD2; LOC152195; LOC100132199; DGAT2; SCML1; LSS; CIITA; SAP30; TLR5; NAMPT; GZMK; CARD17; INCA; MSL3L1; CD8A; MIIP; SRPK1; SLC6A6; C10orf119; C17orf60; LOC642816; AKR1C3; LHFPL2; CR1; KIAA1026; CCDC91; FAM102A; FAM102A; UPRT; PLEKHA1; CACNA2D3; DDX10; RPL23A; C2orf44; LSP1; C7orf53; DNAJC5; SLAIN1; CDKN1C; HIATL1; CRELD1; ZNHIT6; TIFA; ARL4C; PIGU; MEF2A; PIK3CB; CDK5RAP2; FLNB; GRAP; BATF; CYP4F3; KIR2DL3; C19orf59; NRG1; PPP2R2B; CDK5RAP2; PLSCR1; UBL7; HES4; ZNF256; DKFZp761E198; SAMD14; BAG3; PARP14; MS4A7; ECHDC3; OCIAD2; LOC90925; RGL4; PARP9; PARP9; CD151; SAAL1; LOC388076; SIGLEC5; LRIG1; PTGDR; PTGDR; NBPF8; NHS; ACSL1; HK3; SNX20; F2RL1; F2RL1; PARP12; LOC441506; MFGE8; SERPINA10; FAM69A; IL4R; KIAA1671; OAS3; PRR5; TMEM194; MS4A1; MTHFD2; LOC400793; CEACAM1; APP; RRBP1; SLCO4C1; XAF1; XAF1; SLC2A6; ZNF831; ZNF831; POLR1C; GLT1D1; VDR; IFIT5; SNHG8; TOP1MT; UPP1; SYTL2; LOC440359; KLRB1; MTMR3; S1PR1; FYB; CDC20; MEX3C; FAM168B; SLC4A7; CD79B; FAM84B; LOC100134688; LOC651738; PLAGL1; TIMM10; LOC641710; TRAF5; TAP1; FCRL2; SRC; RALGAPA1; OCIAD2; PON2; LOC730029; LOC100134768; LOC100134241; LOC26010; PLA2G12A; BACH1; DSC1; NOB1; LOC645693; LOC643313; BTBD11; REPS2; ZNF23; C18orf55; APOL2; APOL2; PASK; FER1L3; U2AF1; LOC285359; SIGLEC14; ARL1; C19orf62; NCR3; HOXB2; RNF135; IFIT1; KLF12; LILRB2; LOC728835; GSN; LOC100008589; LOC100008589; FLJ14213; SH2D3C; LOC100133177; HIST2H2AB; KIAA1618; C21orf2; CREB5; FAS; RSAD2; ANPEP; C14orf179; TXNL4B; MYL9; MYL9; LOC100130828; LOC391019; ITGA2B; KLRC3; RASGRP2; NDST1; LOC388344; IF16; OAS1; OAS1; TRIM10; LIMK2; LIMK2; ATP5S; SMARCD3; PHC2; SOX8; LCK; SAMD9L; EHBP1; E2F2; CEACAM6; LOC100132394; LOC728014; LOC728014; SIRPG; OPLAH; FTHL2; CXorf21; CACNG6; C11orf75; LY9; LILRB4; STAT2; RAB20; SOCS1; PLOD2; UGDH; MAK16; ITGB3; DHRS9; PLEKHF1; ASAP1IT1; PSME2; LOC100128269; ALX1; BAK1; XPO4; CD247; FAM43A; ICOS; ISG15; HIST2H2AA4; CD79A; SLC25A4; TMEM158; GPR18; LAP3; TNFSF13B; TC2N; HSF2; CD7; C20orf3; HLA-DRB3; SESN1; LOC347376; P2RY14; P2RY14; P2RY14; CYP1B1; IFIT3; IFIT3; RPL13L; LOC729423; DBN1; TTC27; DPH5; GPR141; RBBP8; LOC654350; SLC30A1; PRSS23; JAM3; GNPDA2; IL7R; ACAD11; LOC642788; ALPK1; LOC439949; BCAT1; ATPGD1; TREML1; PECR; SPATA13; MAN1C1; ID01; TSEN54; SCRN1; LOC441193; LOC202134; KIAA0319L; MOSC1; PFKFB3; GNB4; ANKRD22; PROS1; CD40LG; RIOK2; AFF1; HIST1H3D; SLC26A8; SLC26A8; RNASE3; UBE2L6; UBE2L6; SSH1; KRBA1; SLC25A23; DTX3L; DOK3; SULT1B1; RASGRP4; ALOX15B; ADM; LOC391825; LOC730234; HIST2H2AA3; HIST2H2AA3; LIMK2; MMRN1; FKBP1A; GYG1; ASF1A; CD248; CD3G; DEFA1; EPHX2; CST7; ABLIM3; ANKRD55; SLC45A3; RAB33B; LILRA6; LILRA6; SPTLC2; CDA; PGD; LOC100130769; ECHDC2; KIF20B; B3GNT8; PYHIN1; LBH; LBH; BPI; GAR1; ST3GAL4; TMEM19; DHRS12; DHRS12; FAM26F; FCRLA; OSBPL7; CTSB; ALDH1A1; SRRD; TOLLIP; ICAM1; LAX1; CASP7; ZDHHC19; LOC732371; DENND1A; EMR2; LOC643308; ADA; LOC646527; LOC643313; GZMB; OLIG2; HLA-DPB1; MX1; THOC3; TRPM6; GK; JAK2; ARHGEF11; ARHGEF11; HOMER2; TACSTD2; CA4; GAA; IFITM3; CLYBL; CLYBL; MME; ZNF408; STAT1; STAT1; PNPLA7; INDO; PDZD8; PDGFD; CTSL1; HOMER3; CEP78; SBK1; ALG9; IL1R2; RAB40B; MMP23B; PGLYRP1; UHRF1; IF144L; PARP10; PARP10; GOLGA8A; CCR7; HEMGN; TCF7; CLUAP1; LOC390735; LOC641849; TYMP; DEFA1B; DEFA1B; DEFA1B; REPS2; REPS2; OSBPL1A; C11orf1; MCTP2; EMR4; LOC653316; FCRL6; MRPS26; RHOBTB3; DIRC2; CD27; PLEKHG4; CDH6; C4orf23; HIST2H2AC; SLC7A6; SLC7A6; SLAMF6; RETN; FAIM3; TMEM99; LOC728411; TMEM194A; NAPEPLD; ACOX1; CTLA4; SCO2; STK3; FLT3LG; VASP; FBXO31; TDRD9; TDRD9; LOC646144; NUSAP1; GPR97; GPR97; GPR97; EMR1; SLAMF6; CCDC106; ODF3B; LOC100129904; PADI4; LOC100132858; PIK3AP1; ZNF792; DIP2A; OSCAR; CLIC3; FANCE; TECPR2; P2RY10; ADORA3; IL18RAP; DEFA3; BRSK1; LOC647691; S1PR5; CPA3; BMX; DDX58; RHOBTB1; TNFRSF25; LOC730387; OLR1; HERC5; STAT1; NELF; STAP1; ZNF516; ARHGAP26; TIMP2; FCGR1A; RHOH; IF144; MTX3; CD74; LCK; TLR4; DSC2; CXorf45; ENPP4; CD300C; OASL; HPSE; MTHFD2; GSTM2; OLFM4; ABHD12B; LOC728417; LOC728417; FCAR; GTPBP3; KLF4; HOPX; THBD; HIST1H2BG; LOC730995; NOP56; ZBTB9; NLRC3; LOC100134083; COP1; CARD16; SP140; CD96; POLD2; IL32; LOC728744; FZD2; ZAP70; PYHIN1; SCARF1; IF127; PFKFB2; PAM; WARS; TCN1; LOC649839; MMP9; TMEM194A; TAP2; C17orf87; LOC728650; PNMA3; CPT1B; LTBP3; CCDC34; PRAGMIN; C9orf91; SMPDL3A; GPR56; C14orf147; SMARCD3; FAM119A; LOC642334; ENOSF1; FAR2; LOC441763; TESC; CECR6; KIAA1598; GPR109B; LRRN3; RNF213; ASGR2; ASGR2; ZSCAN18; MCOLN2; IFIT2; PLCH2; MAP7; GBP4; MGMT; GAL3ST4; C2orf89; TXNDC3; IFIH1; PRRG4; LOC641693; LOC728093; TNFAIP8L1; AP3M2; BACH2; BACH2; C9orf123; CACNA1I; LOC100132287; CAMK1D; ANKRD33; CCR6; ALDH1A1; LOC100132797; CD163; ESAM; FCAR; TCN2; CD6; CD3E; CCDC76; MS4A1; IFIT1; MED13L; SLC26A8; NOV; FLJ20035; UGT1A3; LOC653600; LOC642684; KIAA0319L; KLRD1; TRIM22; C4orf18; TSPAN3; TSPAN3; DNAJC3; AGTRAP; LOC646786; NCALD; TTC25; TSPAN5; ZNF559; NFKB2; LOC652616; HLA-DOA; WARS; GBP2; AUTS2; IGF2BP3; OASL; DYSF; FLJ43093; MS4A14; TGFB1I1; RAD51C; CALD1; LOC730281; MUC1; C14orf124; RPL14; APOL6; KCTD12; ITGAX; IFIT3; LPCAT2; ZNF529; AGTRAP; LOC402112; LOC100134822; SH2D1B; MPO; LOC100131967; LOC440459; FAM44B; ACOT9; LOC729915; PDZK1IP1; S100A12; RAB3IL1; TMEM204; CXCL10; TSR1; MXD3; LILRA5; CKAP4; C6orf190; ECGF1; LDLRAP1; GRB10; FCRL3; LOC731275; ZFP91; BCL6; SAMD3; LOC647436; CLC; GK; LOC100133565; OAS2; LOC644937; SIRPD; GPBAR1; GNL3; CD79B; ELF2; GAA; CD47; NMT2; MATR3; TMEM107; GCM1; RORA; MGAM; LOC100132491; KRT72; SEPT04; ACADVL; ANXA3; MEGF9; MEGF9; PTPRJ; HLA-DRB4; FFAR2; PML; HLA-DQA1; CEACAM8; SH3KBP1; TRPM2; CUX1; SUV39H1; USF1; VAPA; ALOX15; CD79A; DPRXP4; LOC652750; ECM1; ST6GAL1; KLHL3; RTP4; FAM179A; HDC; SACS; C9orf72; C9orf72; LOC652726; PVRIG; PPP1R16B; NSUN7; NSUN7; ZNF783; LOC441013; LOC100129343; OSM; UNC93B1; DNAJC30; FLJ14166; C9orf72; SAMD4A; F5; PARP15; PAFAH2; COL17A1; TYMP; LOC389672; ABCB1; LOC644852; TARP; SLAMF7; FRMD3; LOC648984; PLAUR; LOC100132119; KLRG1; INTS2; MYC; HIST1H4H; C9orf45; GBP6; KIFAP3; HSPC159; SOCS3; GOLGA8B; LOC100133583; ARL4A; ASNS; ITGAX; LOC153561; GSTM1; OAS2; OAS2; TRIM25; ABHD14A; LOC642342; GPR56; C4orf18; AK1; PIK3R6; HSPE1; ASPHD2; DHRS9; GRN; BOAT; LOC100134300; SDSL; TNFAIP6; LOC402176; LOC441019; FAM134B; ZNF573, GGGGTAACACAGAGTGCCCTTATGAAGGAGTTGGAGATCCTgcaaggaag (SEQ ID NO.:69); AAACCCGTCACCCAGATCGTCAGCGCCGAGGCCTGGGGTAGAGCAGGTGA (SEQ ID NO.:87); TGTTCTTCCCCATGTCCTGGATGCCACTGGAAGTGCACACTGCTTGTATG (SEQ ID NO.:93); CCCTGGAAAGCTCCCCGACAACCTCCACTGCCATTACCCACTAGGCAAGT (SEQ ID NO.:95); CCTCCAGTGGTTTAGGCAGGACCCTGGGAAAGGTCTCACATCTCTGTTGC (SEQ ID NO.:174); GCACCATGCATGGAGTCAGCCATTTCTCTAGGAACCTTGATTCCTGTCTG (SEQ ID NO.:193); CCCCACGCCTGTTTGTATTGGGAGCTCTGGACCAATAGTGTCTCTCCTAG (SEQ ID NO.:196); CCAGCCACTCTACTCAAGGGGCATATATTTTGGCATGAGGTGGGATAGAG (SEQ ID NO.:240); gcatgtgtatgatgtgtgtgcgtcggaccgcttctaggctactaagtgtc (SEQ ID NO.:257); AGGGGCAGTATACTCTTATCAGTGCGAGGTAGCTGGGGCCTGTGATAGTT (SEQ ID NO.:299); CAAGCCTGGCAGTAAATCCGAATATCCAGAACCCTGACCCTGCCGTGTAC (SEQ ID NO.:319); CAGCATGTAGGGCAGTGCTTGCACGTAGCATCTGGTGCCTAACCAGTGTT (SEQ ID NO.:336); CTGAGGTTATGTACAACCAACTCTCAGAATTCAGACTTCCTGCAGCTGCC (SEQ ID NO.:370); GTAGGCCCCCAAAGTGCCGTCTTTCCCTAGCATTTTACTCAATGTTTGCC (SEQ ID NO.:392); GAATCAAGGAGGTCAAGTAAGGTCACAGGGGCACTTGGGTTGAGCCAGGG (SEQ ID NO.:437); CCCCAGATGGTTCCAAATATTCCTTACCTCGTTTGGTTCCCAAGTCACAG (SEQ ID NO.:450); GAATAGAAACCAGACAGCAATTCTTTAGTTCCAGCCACCATTCGCCCCAC (SEQ ID NO.:454); TCAACAAAGAGGTGCTGACCTGAGAGTAGGGCACATAACCTCAGCCACTG (SEQ ID NO.:471); ATGTAGATGGGGAGTGACCACCGCCAACAGAAGTGTGGCCATCTTGCCCG (SEQ ID NO.:535); CTTTGGGCACCATTTGGATATAGTTAGTGGTGGTTTAGCTATGGCGTTCC (SEQ ID NO.:609); GGCAAATTCCGGGTATGCACTCAACTTCGGCAAAGGCACCTCGCTGTTGG (SEQ ID NO.:637); GAGGCTTTCAGGTAGGAGGACAATGGTAGCACTGTAGGTCCCCAGTGTCG (SEQ ID NO.:754); AGTAAACCCATATATCCAGAACCCTGACCCTGCCGTGTACCAGCTGAGAG (SEQ ID NO.:800); CCTGTGGCAAGCCAGCAAGATGGCCCTGGTGACAGCAAAAGAAACTGCAC (SEQ ID NO.:837); CCAGGTGCCGCCCACTCTTGACGTGATACTTACCGTCAATGCTCCTTACC (SEQ ID NO.:876); GCCTAAACCAGGTATGCCAATCTGTCTTGTGTCCACATACTAACAGAGGG (SEQ ID NO.:924); AGCCAAGACAGCAGCTCTACATCCTTACCTAGGTAATTCAGGCATGCGCC (SEQ ID NO.:947); CACATGGCAAATGCCTCCTTTCACAATAGAGCATGGTGCTGTTTCCTCAC (SEQ ID NO.:954); TATTGCAGCCATCCATCTTGGGGGCTCATCCATCACACCCGGGTTGCTAG (SEQ ID NO.:1010); CTGGGCTGTGGTATTTGGGTGATCTTTACATTCTTCAGACTCATGTGTGT (SEQ ID NO.:1035); GCTACAAACAAGCTCATCTTTGGAACTGGCACTCTGCTTGCTGTCCAGCC (SEQ ID NO.:1081); CCTACTCCTACAGTGCCTTGCATTCCGTAGCTGCTCAGTACATTAACCCA (SEQ ID NO.:1116); CAGGGTATGAAAGTGCCCATTTCTAGCCAACATTAGATACCCTCAGTCTC (SEQ ID NO.:1157); TGGCCACATTTGTCTCAAACTCAAGTCTACACATTTCTCTCTCTTTTCCC (SEQ ID NO.:1227); GTACCGTCAGCAACCTGGACAGAGCCTGACACTGATCGCAACTGCAAATC (SEQ ID NO.:1276); and Gccccctaattgactgaatggaacccctcttgaccaaagtgaccccagaa (SEQ ID NO.:1379).
 28. The method of claim 25, further comprising the step of differentiating between sarcoidosis and tuberculosis, lung cancer or pneumonia by determining the expression levels of the following genes, markers, or probes: PHF20L1; LOC400304; SELM; DPM2; RPLP1; SF1; ZNF683; CTTN; PTCRA; SNORA28; RPGRIP1; GPR160; PPIA; DNASE1L1; HEMGN; RAB13; NFIA; LOC728843; LOC100134660; LOC100132564; HIP1; PRMT1; PDGFC; NCRNA00085; NFATC3; GIMAP7; LOC100130905; AKAP7; TLE3; NRSN2; RPL37; CSTA; C20orf107; TMEM169; GCAT; TMEM176A; CMTM5; C3orf26; FANCD2; C9orf114; TIAM2; LOC644615; PADI2; GRINA; CHST13; ANGPT1; KIF27; ZNF550; PIK3C2A; NR1H3; ALG8; SLC2A5; ITGB5; OPN3; UBE2O; RIN3; LOC100129203; B3GNT1; NEK8; SLC38A5; GPR183; LOC728748; LOC646966; FAM159A; LOC441073; CCNC; MRPL9; SLC37A1; NSUN5; GHRL; ALAS2; MPZL2; RNF13; SUMO1P1; UHRF2; RNY4; LOC651524; ZNF224; OLIG1; TNFRSF4; BEND7; LOC728323; ARHGAP24; CCCTGCCCTCATGTTGCTTTGGGTCTAGTGGAGGAGAGAGACAGATAAGC (SEQ ID NO.:1447); CAAGTTCTTAACCATCCCGGGTTCCAGTGGTTACAGAGTTCTGCCCTGGG; (SEQ ID NO.:1448) and TGCATGAGATCACACAACTAGGCGGTGACTGAGTCCAACACACCAAAGCC (SEQ ID NO.:1449).
 29. The method of claim 25, further comprising the step of differentiating between sarcoidosis that is active and sarcoidosis that is inactive by determining the expression levels of the following genes, markers, or probes: LOC442132; HOXA1; LOC652102; PPIE; C22orf27; TEX10; LMTK2; LOC283663; SUCNR1; COLQ; HLA-DOB; SAMSN1; INPP5E; CYP4F3; CRYZ; CDC14A; LOC653061; KIR2DL4; PCYOX1L; TCEAL3; FRRS1; PHF17; PDK4; LOC440313; ZNF260; SLFN13; VASH1; GM2A; ASAP2; VARS2; RPL14; KIR2DL1; SBDSP; S1PR3; and METTL1; CCAGGAGGCCGAACACTTCTTTCTGCTTTCTTGACATCCGCTCACCAGGC (SEQ ID NO.:1452), and TTCCAGGGCACGAGTTCGAGGCCAGCCTGGTCCACATGGGTCGGaaaaaa (SEQ ID NO.:1451).
 30. The method of claim 25, further comprising the step of using 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 144, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1,000, 1,100, 1,200, 1,300, 1,400, or 1,446 genes selected from SEQ ID NOS.: 1 to 1446 to determine if the patient has at least one of tuberculosis, sarcoidosis, cancer or pneumonia.
 31. A method for determining the effectiveness of a treating a sarcoidosis patient comprising: obtaining a sample from a subject suspected of having a pulmonary disease; determining the expression level of 3, 4, 5, 6 or more genes selected from IL1R2; GRB10; CEACAM4; SIPA1L2; BMX; IL1RAP; REPS2; ANXA3; MMP9; PHC2; HAUS4; DUSP1; CA4; SAMSN1; KLHL2; ACSL1; NSUN7; IL18RAP; GNG10; SMAP2; MGAM; LIN7A; IRAK3; USP10; CEBPD; TGFA; FOS; MANSC1; SLC26A8; ROPN1L; GPR97; NAMPT; MRVI1; KCNJ15; KLHL8; GNG10; MEGF9; GPR160; B4GALT5; STEAP4; LRG1; F5; PHTF1; HMGB2; DGAT2; SLC11A1; QPCT; PANX2; GPR141; or LMNB1; wherein overexpression of the genes is indicative of a reduction in sarcoidosis.
 32. A method of identifying a subject with a pulmonary disease comprising: obtaining a sample from a subject suspected of having a pulmonary disease; determining the expression level of six or more genes from each of the following genes selected from: UBE2J2; ALPL; JMJD6; FCER1G; LILRA5; LY96; FCGR1C; C10orf33; GPR109B; PROK2; PIM3; SH3GLB1; DUSP3; PPAP2C; SLPI; MCTP1; KIF1B; FLJ32255; BAGE5; IFITM1; GPR109A; IF135; LOC653591; KREMEN1; IL18R1; CACNA1E; ABCA2; CEACAM1; MXD4; TncRNA; LMNB1; H2AFJ; HP; ZNF438; FCER1A; SLC22A4; DISC1; MEFV; ABCA1; ITPRIPL2; KCNJ15; LOC728519; ERLIN1; NLRC4; B4GALT5; LOC653610; HIST2H2BE; AIM2; P2RY10; CCR3; EMR4P; NTN3; C1QB; TAOK1; FCGR1B; GATA2; FKBP5; DGAT2; TLR5; CARD17; INCA; MSL3L1; ESPN; LOC645159; C19orf59; CDK5RAP2; PLSCR1; RGL4; IFI30; LOC641710; GAGGCTTTCAGGTAGGAGGACAATGGTAGCACTGTAGGTCCCCAGTGTCG (SEQ ID NO.: 754); LOC100008589; LOC100008589; SMARCD3; NGFRAP1; LOC100132394; OPLAH; CACNG6; LILRB4; HIST2H2AA4; CYP1B1; PGS1; SPATA13; PFKFB3; HIST1H3D; SNORA73B; SLC26A8; SULT1B1; ADM; HIST2H2AA3; HIST2H2AA3; GYG1; CST7; EMR4; LILRA6; MEF2D; IFITM3; MSL3; DHRS13; EMR4; C16orf57; HIST2H2AC; EEF1D; TDRD9; GPR97; ZNF792; LOC100134364; SRGAP3; FCGR1A; HPSE; LOC728417; LOC728417; MIR21; HIST1H2BG; COP1; SMARCD3; LOC441763; ZSCAN18; GNG8; MTRF1L; ANKRD33; PLAC8; PLAC8; SLC26A8; AGTRAP; FLJ43093; LPCAT2; AGTRAP; S100A12; SVIL; LILRA5; LILRA5; ZFP91; CLC; LOC100133565; LTB4R; SEPT04; ANXA3; BHLHB2; IL4R; IFNAR1; MAZ; gccccctaattgactgaatggaacccctcttgaccaaagtgaccccagaa (SEQ ID NO.: 1379); comparing the expression level of the 3, 4, 5, 6 or more genes with the expression level of the same genes from individuals not afflicted with a pulmonary disease, and determining the level of expression of the six or more genes in the sample from the subject relative to the samples from individuals not afflicted with a pulmonary disease for the genes expressed in the one or more expression pathways, selected from: EIF2 signaling and mTOR signaling pathways are indicative of active sarcoidosis; co-expression of genes in the regulation of eIF4 and p70s6K signaling pathways is indicative of pneumonia; co-expression of genes in the interferon signaling and antigen presentation pathways are indicative of tuberculosis; and co-expression of genes in the T cell signaling pathways; and other signaling pathways is indicative of lung cancer.
 33. The method of claim 32, wherein the genes that are downregulated are selected from MEF2D; BHLHB2; CLC; FCER1A; SRGAP3; FLJ43093; CCR3; EMR4; ZNF792; C10orf33; CACNG6; P2RY10; GATA2; EMR4P; ESPN; EMR4; MXD4; and ZSCAN18.
 34. The method of claim 32, further comprising a method for displaying if the patient has tuberculosis, sarcoidosis, cancer or pneumonia by aggregating the expression data from the six or more genes into a single visual display of a vector of expression for tuberculosis, sarcoidosis, cancer or pneumonia.
 35. The method of claim 32, further comprising the step of detecting and evaluating 7, 8, 9, 10, 12, 15, 20, 25, 35, 50, 75, 90, 100, 125, or 144 genes for the analysis.
 36. The method of claim 32, wherein the sample is a blood, peripheral blood mononuclear cells, sputum, or lung biopsy.
 37. The method of claim 32, wherein the expression level comprises an mRNA expression level and is quantitated by a method selected from the group consisting of polymerase chain reaction, real time polymerase chain reaction, reverse transcriptase polymerase chain reaction, hybridization, probe hybridization and gene expression array.
 38. The method of claim 32, wherein the expression level is determined using at least one technique selected from polymerase chain reaction, heteroduplex analysis, single stand conformational polymorphism analysis, ligase chain reaction, comparative genome hybridization, Southern blotting, Northern blotting, Western blotting, enzyme-linked immunosorbent assay, fluorescent resonance energy-transfer and sequencing.
 39. The method of claim 32, wherein the expression level is determined by microarray analysis that comprises use of oligonucleotides that hybridize to mRNA transcripts or cDNAs for the six or more genes, and wherein the oligonucleotides are disposed or directly synthesized on the surface of a chip or wafer.
 40. The method of claim 39, wherein the oligonucleotides are about 10 to about 50 nucleotides in length.
 41. The method of claim 32, further comprising the step of using the determined comparative gene product information to formulate at least one of diagnosis, a prognosis or a treatment plan.
 42. The method of claim 32, wherein the patient's disease state is further determined by radiological analysis of the patient's lungs.
 43. The method of claim 32, further comprising the step of determining a treated patient gene expression dataset after the patient has been treated and determining if the treated patient gene expression dataset has returned to a normal gene or a changed gene expression dataset thereby determining if the patient has been treated.
 44. The method of claim 32, wherein a non-overlapping set of genes is used to distinguish between Tb, sarcoidosis, pneumonia and lung cancer, versus, Tb, active sarcoidosis, non-active sarcoidosis, pneumonia and lung cancer are selected from Table 11, 12 or both.
 45. A computer readable medium comprising computer-executable instructions for performing the method of claim
 1. 